KR102015082B1 - syntax-based method of providing object tracking in compressed video - Google Patents

Abstract

The present invention generally relates to a technique for efficiently performing object tracking from compressed images such as H.264 AVC and H.265 HEVC. More specifically, the present invention, for example, in the image block unit constituting the compressed image without having to perform a complex image processing in the area where there is something meaningful movement, that is, the moving object region for the compressed image generated by the CCTV camera, for example The present invention relates to a technique for extracting based on syntax, such as a low motion vector and a coding type, and tracking the movement of the moving object region to provide normalization to an image control apparatus. According to the present invention, it is possible to quickly track a moving object region in CCTV photographed images without complex processing such as decoding, downscaling resizing, difference image acquisition, image analysis, etc. of the compressed image, thereby preventing crime through the video control system and There is an advantage that can increase the effect of securing post evidence. In this case, by providing a video control device after mathematically normalizing the moving object tracking result, there is an advantage of ensuring compatibility with various equipment.

Description

Syntax-based method of providing object tracking in compressed video

The present invention generally relates to a technique for efficiently performing object tracking from compressed images such as H.264 AVC and H.265 HEVC.

More specifically, the present invention, for example, in the image block unit constituting the compressed image without having to perform a complex image processing in the area where there is something meaningful movement, that is, the moving object region for the compressed image generated by the CCTV camera, for example The present invention relates to a technique for extracting based on syntax, such as a low motion vector and a coding type, and tracking the movement of the moving object region to provide normalization to an image control apparatus.

Recently, it is common to build a video control system using CCTV for crime prevention and security. With multiple CCTV cameras installed by region, images generated by these CCTV cameras are displayed on a monitor and stored in a storage device. When a control agent finds a scene where a crime or accident occurs, he or she immediately responds appropriately and, if necessary, retrieves the image stored in the storage to secure post evidence.

By the way. The reality is that the number of control personnel is very low compared to the installation status of CCTV cameras. In order to effectively perform video surveillance with such limited number of people, simply displaying CCTV images on the monitor screen is not enough. It is preferable to process the object to be effectively detected by detecting the motion of an object present in each CCTV image and displaying something in the corresponding area in real time. In this case, the monitoring personnel do not monitor the entire CCTV image with uniform interest, but monitor the CCTV image centering on the part where the object moves.

On the other hand, the video sensing system adopts compressed video for the efficiency of the storage space. In particular, as the number of CCTV cameras is rapidly increasing and high quality cameras are mainly installed, high-compression complex video compression technologies such as H.264 AVC and H.265 HEVC have been adopted.

The camera device generating the video data generates and provides a compressed image according to any one of these technical standards, and when the device playing video receives the compressed image, the camera device generates the compressed data according to the technical standard applied when encoding the compressed image. Perform decoding. In order to determine the presence or absence of motion in a CCTV image to which image compression technology is applied, conventionally, a process of processing an image after decompressing a compressed image and obtaining a reproduced image, that is, an uncompressed original image is required.

1 is a block diagram illustrating a general configuration of a video decoding apparatus according to the H.264 AVC Technical Standard. Referring to FIG. 1, a video decoding apparatus according to H.264 AVC may include a parser 11, an entropy decoder 12, an inverse converter 13, a motion vector operator 14, a predictor 15, and deblocking. And a filter 16.

These hardware modules process compressed video sequentially to decompress and restore the original video data. At this time, the parser 11 parses the motion vector and the coding type for the coding unit of the compressed image. Such a coding unit is generally an image block such as a macroblock or a subblock, and may be implemented not exactly according to a technical standard.

2 is a flowchart illustrating a process of performing object tracking from a compressed image in a conventional image analysis solution.

Referring to FIG. 2, in the prior art, the compressed video is decoded according to H.264 AVC and H.265 HEVC, etc. (S10), and the frame images of the playback video are downscaled to a small image, for example, 320x240 (S20). ). In this case, the reason for downsizing resizing is to reduce the processing burden in the subsequent process. Then, after obtaining differential images of the resized frame images, the moving object is extracted through image analysis (S30). Then, the moving path of the moving object is identified through image analysis of the series of frame images (S40).

In the prior art, to extract a moving object, compressed image decoding, downscale resizing, and image analysis are performed. These are very complicated processes, and therefore, in a conventional video control system, the capacity of a single video analysis server can be processed at a time is quite limited. Currently, the maximum CCTV channels that a high performance video analytics server can cover are typically up to 16 channels. Since a large number of CCTV cameras were installed, a number of video analysis servers were required for the video control system, which caused problems of increased cost and difficulty in securing physical space.

The construction and maintenance of a large-scale video control system requires a considerable budget, and a corresponding utility value is required. The basic direction of such a demand is crime prevention and criminal evidence. Therefore, it can go beyond simply photographing and storing the surrounding image or notifying the existence of a moving object from it, and providing a high level of detection that the video control system detects a special situation where the experience itself is a problem in real life. There is a need. At this time, an efficient implementation technology is also required in consideration of the actual problems of system construction cost and physical space.

SUMMARY OF THE INVENTION An object of the present invention is to provide a technique for efficiently performing object tracking from compressed video such as H.264 AVC and H.265 HEVC.

In particular, an object of the present invention is to, for example, the area in which something meaningful movements exist for the compressed image generated by the CCTV camera, that is, the moving object region in units of image blocks constituting the compressed image without having to perform complicated image processing as in the prior art. The present invention provides a technique for extracting based on syntax, such as a motion vector and a coding type, and tracking the movement of the moving object region to provide normalization to an image control apparatus.

In order to achieve the above object, a syntax-based object tracking method for a compressed image according to the present invention includes: a first step of parsing a bitstream of a compressed image to obtain a motion vector and a coding type for a coding unit; A second step of obtaining a motion vector cumulative value for a first preset time for each of the plurality of image blocks constituting the compressed image; A third step of comparing a motion vector cumulative value with a first threshold value for a plurality of image blocks; A fourth step of marking an image block having a motion vector accumulation value exceeding a first threshold as a moving object region; With respect to the moving object region (hereinafter, referred to as the "tracking target moving object region") specified as the tracking target by a user operation, a series of coordinates of the tracking target moving object region are obtained over a series of image frames of the compressed image. And a fifth step of providing the coordinate sequence according to the image control apparatus.

In the present invention, the image block constituting the compressed image may include a macroblock and a subblock.

In this case, the fifth step may include: a fifth step of newly issuing and assigning a unique ID when the moving object region is in an ID unassigned state; A fifth step of setting a specific moving object area (hereinafter referred to as a 'tracking target moving object area') as a tracking object according to a user's operation; A fifth step of identifying a unique ID (hereinafter, referred to as a 'tracking unique ID') allocated to the tracked moving object region; A fifth step of sequentially calculating rectangular coordinates of the moving object region to which the tracking unique ID value is assigned to the series of image frames constituting the compressed image and setting the coordinates of the moving object region to the tracking sequence; A fifth step of normalizing the series of rectangular coordinates included in the coordinate sequence of the region to be tracked corresponding to the resolution of the compressed image; A fifth step of providing the video sequence apparatus with the coordinate sequence of the region of the tracking target moving object normalized above; And revoking the assigned Unique ID when the moving object region disappears from the series of image frames.

In this case, the rectangular coordinates include upper left coordinates (x, y), horizontal axis lengths (dx), and vertical axis lengths (dy) of a virtually formed rectangle so as to optimally include a moving object region. The upper left x coordinate and horizontal axis length (dx) are divided by the horizontal resolution (x_res) of the compressed image, reflecting the horizontal resolution (x_res) and vertical resolution (y_res), and the upper left y coordinate and vertical axis length (dy) are vertically compressed. It is preferable to divide by the resolution (y_res).

In addition, the object tracking method according to the present invention comprises a first step of identifying a plurality of adjacent image blocks (hereinafter referred to as 'neighbor block') around the moving object area; B) comparing a motion vector value with a second preset threshold value for a plurality of neighboring blocks; C) additionally marking a neighboring block having a motion vector value exceeding a second threshold as a moving object region; D) additionally marking a neighboring block having a coding type of an intra picture among the plurality of neighboring blocks as a moving object region; The method may further include an e-step of performing interpolation on the plurality of moving object regions to additionally mark the moving object region with a predetermined number of non-marked image blocks surrounded by the moving object region.

Meanwhile, the computer-readable nonvolatile recording medium according to the present invention records a program for executing a syntax-based object tracking method for the compressed image as described above on a computer.

According to the present invention, since the moving object region is extracted from the CCTV image without complex processing such as decoding, downscaling resizing, difference image acquisition, image analysis, etc., the CCTV compressed image is about 20 times higher than that of the existing image analysis server. There is an advantage to the performance gains.

In addition, according to the present invention, it is possible to quickly track the moving object area in CCTV images without complex processing such as decoding, downscale resizing, difference image acquisition, image analysis, etc. of the compressed image. There is an advantage to increase the effect of preventing and securing evidence. In this case, by providing a video control device after mathematically normalizing the moving object tracking result, there is an advantage of ensuring compatibility with various equipment.

1 is a block diagram showing a general configuration of a video decoding apparatus.
2 is a flowchart illustrating a process of performing object tracking from a compressed image in the prior art.
3 is a flowchart illustrating the entire process of performing object tracking from a compressed image according to the present invention.
4 is a flowchart showing an embodiment of a process of detecting an effective motion from a compressed image in the present invention.
5 is a diagram illustrating an example of a result of applying an effective motion region detection process according to the present invention to a CCTV compressed image.
FIG. 6 is a flowchart illustrating an example of a process of detecting a boundary region for a moving object region in the present invention. FIG.
FIG. 7 is a diagram illustrating an example of a result of applying a boundary area detection process according to the present invention to the CCTV image of FIG. 5. FIG.
FIG. 8 is a diagram illustrating an example of a result of arranging a moving object region through interpolation for the CCTV video image of FIG. 7. FIG.
FIG. 9 is a flowchart illustrating an example of a process of tracking and identifying a region of a tracking target moving object designated by a user from a compressed image in the present invention. FIG.
FIG. 10 is a diagram illustrating an example in which a unique ID is assigned to a moving object area in the present invention. FIG.
FIG. 11 is a diagram illustrating an example in which rectangular coordinates are set in a moving object region in the present invention. FIG.

Hereinafter, with reference to the drawings will be described in detail the present invention.

3 is a flowchart illustrating the entire process of performing object tracking from a compressed image according to the present invention. In the object tracking process according to the present invention, a video analysis server may be preferably performed in a system for handling a series of compressed images, for example, a CCTV video control system.

The present invention parses a bitstream of a compressed image without having to decode the compressed image, thereby syntax information of each image block, that is, a macro block and a sub block, preferably a motion vector. Quickly extract the moving object region through and coding type information. The moving object region thus obtained does not accurately reflect the boundary of the moving object as shown in the image attached to the present specification, but has a high processing speed and high reliability. Then, the present invention performs an operation of tracking the specific moving object area designated by the controller based on the obtained moving object area.

Meanwhile, according to the present invention, the moving object region can be extracted and the object tracking can be performed without decoding the compressed image. However, the apparatus or software to which the present invention is applied should not perform an operation of decoding a compressed image, but the scope of the present invention is not limited.

Hereinafter, a concept of a process of performing object tracking from a compressed image according to the present invention will be described with reference to FIG. 3.

Step S100: First, an effective motion that can substantially recognize meaning is detected from the compressed image based on the motion vector of the compressed image, and the image region in which the effective motion is detected is set as the moving object region.

To this end, the motion vector and coding type of a coding unit of a compressed image are parsed according to a video compression standard such as H.264 AVC and H.265 HEVC. In this case, the size of the coding unit is generally about 64x64 pixels to 4x4 pixels and may be set to be flexible.

The motion vectors are accumulated for a predetermined time period (for example, 500 msec) for each image block, and it is checked whether the motion vector accumulation value exceeds the first threshold value (for example, 20). If such an image block is found, it is considered that an effective motion is found in the image block, and then marked as a moving object area. Accordingly, even if the motion vector is generated, if the cumulative value for a predetermined time does not exceed the first threshold, the image change is estimated to be insignificant and ignored.

Step S200: Next, the moving object area detected in the previous step S100 is detected based on the motion vector and the coding type. To this end, a plurality of adjacent video blocks are examined centering on the image blocks marked as the moving object region, and when the motion vector occurs more than a second threshold (for example, 0) or when the coding type is an intra picture, the corresponding video Blocks are also marked as moving object areas. Through this process, the corresponding image block is substantially in the form of forming a lump with the moving object region detected in S100.

If an effective motion is found and there is a certain amount of motion in the vicinity of the moving object area, it is marked as a moving object area because it is likely to be a mass with the previous moving object area. In addition, in the case of an intra picture, since a motion vector does not exist, determination based on a motion vector is impossible. Accordingly, the intra picture located adjacent to the image block already detected as the moving object region is estimated as a mass together with the previously extracted moving object region.

Step S300: The interpolation is applied to the moving object areas detected at S100 and S200 to clean up the fragmentation of the moving object area. In the above process, since it is determined whether the moving object area is the image block unit, even though it is actually a moving object (for example, a person), there is an image block that is not marked as the moving object area in the middle. The phenomenon of dividing into may occur. Accordingly, if there is one or a few unmarked image blocks surrounded by a plurality of image blocks marked with the moving object region, they additionally mark the moving object region. Through this, it is possible to make the mobile object area divided into several groups into one. The influence of such interpolation is clearly seen when comparing [FIG. 7] and [FIG. 8].

Step S400: The moving object region is quickly extracted based on the syntax (motion vector, coding type) of the coding unit through the above process. In the step S400, when the control agent requests the object tracking while specifying the moving object area through a mouse operation or the like on the monitor screen by using the extracted result of the moving object area, the moving path of the moving object area in the compressed image is determined. Trace it. Since this process is important in real time, the present invention can be well applied. If there is something suspicious in the control agent's judgment in the video surveillance system, it is intended to increase the crime prevention effect by tracking and displaying it. In addition, the object tracking information may be usefully used in terms of securing post evidence.

To this end, in the present invention, a series of image frames (e.g., a sequence of 30 frames per second) of a compressed image in relation to a moving object region (hereinafter, referred to as a 'tracking target moving object region') specified as a tracking target by a user manipulation. Obtain a set of coordinates for the area of the tracked moving object over. Then, the obtained series of coordinates, that is, the coordinate sequence, is provided to the video control device as a tracking result for the moving object region. The video surveillance apparatus displays the moving object area corresponding to the corresponding coordinate in each image frame in a prominent manner to the control personnel.

A detailed process of performing object tracking from the compressed image will be described later in detail with reference to FIG. 9.

4 is a flowchart illustrating an example of a process of detecting effective motion from a compressed image in the present invention, and FIG. 5 is an example of a result of applying an effective motion region detection process according to the present invention to a CCTV compressed image. It is a figure which shows. The process of FIG. 4 corresponds to step S100 in FIG.

Step S110: First, a coding unit of a compressed image is parsed to obtain a motion vector and a coding type. Referring to FIG. 1, a video decoding apparatus performs parsing (header parsing) and motion vector calculation on a stream of a compressed video according to a video compression standard such as H.264 AVC and H.265 HEVC. Through this process, the motion vector and coding type are parsed for the coding unit of the compressed image.

Step S120: Acquire a motion vector cumulative value for a preset time (for example, 500 ms) for each of the plurality of image blocks constituting the compressed image.

This step is presented with the intention to detect if there are effective movements that are practically meaningful from the compressed image, such as driving cars, running people, and fighting crowds. Shaky leaves, ghosts that appear momentarily, and shadows that change slightly due to light reflections are not detected because they are moving but practically meaningless objects.

To this end, a motion vector cumulative value is obtained by accumulating the motion vectors in units of one or more image blocks for a predetermined time period (for example, 500 msec). In this case, the image block is used as a concept including a macroblock and a subblock.

Steps S130 and S140: Comparing a motion vector cumulative value with respect to a plurality of image blocks with a preset first threshold value (eg, 20) and moving the image block having a motion vector cumulative value exceeding the first threshold value. Mark with

If an image block having a predetermined motion vector accumulation value is found as described above, it is regarded that something significant motion, that is, effective motion, is found in the image block, and then marked as a moving object region. For example, in a video surveillance system, a human run is to detect and detect a movement that is worth the attention of the control personnel. On the contrary, even if the motion vector is generated, if the cumulative value for a predetermined time is small enough not to exceed the first threshold, the change in the image is assumed to be small and insignificant and is neglected in the detection step.

Step S150: A display is provided on the playback screen of the compressed image to distinguish the moving object region from the normal image. FIG. 5 is a diagram illustrating an example of a result of an effective motion region detection process according to the present invention, in which a plurality of image blocks representing a motion vector accumulation value exceeding a first threshold is marked as a moving object region and displayed on a monitor screen. It is shown in red. Referring to FIG. 5, the sidewalk block, the road, and the shadowed part are not displayed as the moving object area, while the walking people or the driving car are displayed as the moving object area.

FIG. 6 is a flowchart illustrating an example of a process of detecting a boundary region of a moving object region in the present invention, and FIG. 7 is a CCTV image image to which the effective motion region detecting process shown in FIG. 5 is applied. 6 is a diagram illustrating an example of a result of applying a boundary area detection process according to FIG. 6. The process of FIG. 6 corresponds to step S200 in FIG.

Looking at the previous [Fig. 5] it can be found that the moving object is not properly marked and only a portion of the marking is made. In other words, if you look at a person walking or driving a car, you will find that not all of the objects are marked, but only some blocks. In addition, it is found that a plurality of moving object areas are marked for one moving object. This means that the criterion of the moving object region adopted in S100 was very useful for filtering out the general region but was quite strict. Therefore, it is necessary to detect the boundary of the moving object by looking around the moving object area.

Step S210: First, a plurality of adjacent image blocks are identified based on the image blocks marked as moving object areas by the previous S100. These are referred to as 'neighborhood blocks' in the present specification. These neighboring blocks are parts that are not marked as moving object areas by (S100). In the process of FIG. 6, the neighboring blocks are examined in detail to determine whether any of these neighboring blocks may be included in the boundary of the moving object area. .

Steps S220 and S230: compare a motion vector value with respect to a plurality of neighboring blocks with a second preset threshold (eg, 0) and mark the neighboring block having a motion vector value exceeding the second threshold as a moving object region. do. If the motion is located adjacent to the recognized moving object area and the motion is found to some extent, the image block is likely to be a block with the previous moving object area due to the characteristics of the captured image. Therefore, such neighboring blocks are also marked as moving object regions.

Step S240: Also, the coding type is an intra picture among the plurality of neighboring blocks, as a moving object region. In the case of an intra picture, since a motion vector does not exist, it is fundamentally impossible to determine whether a motion exists in a corresponding neighboring block based on the motion vector. In this case, it is safer for the intra picture located adjacent to the image block already detected as the moving object region to maintain the setting of the previously extracted moving object region.

In operation S250, a display of the moving object region is provided on the reproduction screen of the compressed image so as to be distinguished from the normal image. 7 is a diagram illustrating an example of a result applied to a boundary region detection process in the present invention, in which a plurality of image blocks marked as moving object regions are displayed in blue on a monitor screen. Referring to FIG. 7, the blue moving object region was further extended to the vicinity of the moving object region, which was previously indicated in red in FIG. 5, and thus, it was found that the moving object region covered the entire moving object. have.

FIG. 8 is a diagram illustrating an example of a result of arranging a moving object region through interpolation according to the present invention for a CCTV image image to which the boundary region detection process illustrated in FIG. 7 is applied.

Step S300 is a process of arranging the division of the moving object area by applying interpolation to the moving object areas detected in the previous steps S100 and S200. Referring to FIG. 7, unmarked image blocks are found between the moving object regions indicated in blue. If there are non-marked image blocks in the middle, it is difficult to determine whether they are individual moving objects or objects to be regarded as a mass. In particular, since the mottled display on the monitor screen of the CCTV video control system has a disadvantage that it is difficult for the control personnel to immediately grasp. Furthermore, if the moving object region is fragmented, the result of step S400 may be inaccurate, and in particular, the process of step S400 becomes complicated because the number of moving object regions increases.

Accordingly, in the present invention, if there is one or a few unmarked image blocks surrounded by a plurality of image blocks marked as the moving object region, this is marked as the moving object region, which is called interpolation. Referring to FIG. 8, in contrast to FIG. 7, all of the non-marked image blocks existing between the moving object regions are marked as moving object regions. Through this, it is possible to derive the result of detecting the moving object more intuitively and accurately for the control personnel.

FIG. 9 is a flowchart illustrating an example of a process of tracking and identifying a region of a tracked moving object specified by a user from a compressed image according to the present invention, and corresponds to step S400 in FIG. 3.

As described above, the present invention extracts a moving object region based on syntax information directly obtained from a coding unit of a compressed image. The process of acquiring and analyzing the difference image with respect to the original image by decoding the compressed image of the prior art is unnecessary, and according to the inventor's test, the processing speed is improved up to 20 times. However, this approach has the disadvantage of poor precision. There is a difference in concept in that it extracts the chunk of the image block that is assumed to contain the moving object rather than extracting the moving object itself. Reflecting these differences, the present invention adopts a different approach from the prior art even in the process of tracking a specific object designated by a controller in a CCTV photographing image over time.

Hereinafter, an embodiment of the object tracking process adopted in the present invention will be described in detail.

Step S410: First, in order to treat the moving object area as a single object (object), if a moving object area that is not assigned with identification information (ID) is found, a unique ID is newly issued and assigned. That is, in the previous process, the chunks of the image blocks connected to each other, which are marked as moving object regions, are treated as one object (object). In order to implement this in the software processing process, a unique ID is assigned to the moving object area (a block of image blocks) and managed.

Accordingly, the subsequent process in FIG. 9 is preferably performed based on the Unique ID assigned to the moving object area. 10 illustrates an example in which a unique ID is allocated to a moving object area.

Meanwhile, in step S410, it should be possible to determine whether the chunks of the connected image blocks marked as moving object regions are the same before and after the series of image frames. This is because it is possible to determine whether a Unique ID has been previously assigned to the mobile object area currently being handled.

In the present invention, not only the content of the original video image is checked, but only whether the image block is a moving object region, so it is not possible to accurately determine whether the mass of the moving object region is identical in the front and rear image frames. That is, since the contents of the image included in the image are not known, such a change cannot be identified, for example, when the cat is replaced by a dog between the frames before and after the same point. However, given that the time interval between the frames is very short and that the target of the video control system moves at a normal speed, this is unlikely to happen.

Accordingly, the present invention estimates that the ratio or number of image blocks overlapping between the chunks of the moving object region in the front and back frames is equal to or greater than a predetermined threshold. According to this approach, it is possible to determine whether a specific moving object area is moving, a new moving object area is newly displayed, or an existing moving object area disappears even if the contents of the original image are not known. Although this accuracy is lower than that of the prior art, the data processing speed can be drastically increased, which shows an advantage in actual application.

Steps S420 and S430, a specific moving object area is then set as the tracking target in response to the operation of a user, for example, a CCTV controller. Assuming that the agent is looking at the CCTV footage and found that the offender is running on the screen, you can specify that the offender be tracked on the CCTV monitor screen. In the present invention, rather than tracking criminals, they identify the area of the moving object to which the criminal belongs, and set the tracking target. In the present specification, this region is referred to as a 'tracking target moving object region'.

As described above, in order to manage the identity of the moving object region over time in a series of image frames constituting the compressed image, the moving object region may be treated based on a unique ID. Accordingly, the present invention identifies a unique ID assigned to the tracked moving object region, and in this specification, the unique ID is referred to as a 'tracking unique ID'.

Step S440: Next, one or more moving object regions found in each of the series of image frames constituting the compressed image are examined. Unique IDs are assigned to these mobile object areas, and among them, the mobile object areas to which unique ID values identical to the tracked unique ID values are assigned are identified. The identified moving object area corresponds to the tracking target object area, and the rectangular coordinates of the identified moving object area are sequentially calculated in each image frame. The set of rectangular coordinates thus obtained is set as the coordinate sequence for the tracked moving object region.

Preferably, the rectangular coordinates are calculated for the tracked moving object region. As an example of the rectangular coordinates for the moving object area, the upper left coordinates (x, y), the horizontal axis length (dx), and the vertical axis length (dy) of the virtually formed rectangle may be configured to optimally include the moving object area. have. That is, the rectangular coordinates of the moving object area are in the form of (x, y, dx, dy). 11 illustrates an example in which rectangular coordinates are set in three moving object areas (Unique ID = 001, 002, and 003).

Steps S450 and S460 then normalize a series of rectangular coordinates included in the coordinate sequence of the tracked moving object region corresponding to the resolution of the compressed image. The normalization process is to map to a specific range of values, for example, a real value between 0 and 1. In the present invention, it is preferable to be employed to overcome the differences according to the resolution of the video monitor and to maintain compatibility.

One example of such normalization is dividing the rectangular coordinates into horizontal and vertical resolutions (x_res, y_res) of the compressed image. That is, the upper left x coordinate and the horizontal axis length (dx) of the rectangular coordinates are divided by the horizontal resolution (x_res) of the compressed image, and the upper left y coordinate and the vertical axis length (dy) of the square coordinates are the vertical resolution (y_res) of the compressed image. It's a division process. This normalizes all values to real values between 0 and 1. For example, if the resolution of the compressed image is 100 horizontal and 100 vertical and the square coordinates are (0, 0, 50, 50), the coordinate value becomes (0.0, 0.0, 0.5, 0.5) after normalization processing. This normalization process is applied to a series of rectangular coordinates included in the coordinate sequence.

Subsequently, the coordinate sequence of the normalized tracked moving object area is provided to the video control device so that the state of the tracked moving object area is revealed on the monitor handled by the CCTV controller. As described above, the normalized rectangular coordinates have an advantage of always providing a constant appearance regardless of the display resolution of the monitor in the image control apparatus.

In operation S470, the moving object region corresponding to the coordinate sequence of the tracking target moving object region for each image frame is provided on the reproduction screen of the compressed image so as to be distinguished from the general image. Since the coordinate sequence includes the rectangular coordinates, the corresponding rectangular region may be specially displayed as a whole, or the moving object region optimally disposed in the rectangular region may be specially displayed.

For example, it is preferable to display the moving object area currently being tracked in a special color. At this time, it is preferable to assign different colors to the tracking object to distinguish from each other. Through this, the control agent of the video control system can immediately recognize the image point performing the object tracking, through which the observation with higher attention. This can also help in the process of securing follow-up evidence.

In operation S480, when the moving object region disappears in a series of image frames, the moving object region is destroyed by revoking the Unique ID allocated in step S410 to the moving object region.

Meanwhile, the present invention may be embodied in the form of computer readable codes on a computer readable nonvolatile recording medium. Such nonvolatile recording media include all types of storage devices that store computer readable data, such as hard disks, SSDs, CD-ROMs, NAS, magnetic tapes, web disks, cloud disks, etc. The code may be implemented in a form in which the code is distributed and stored in a storage device.

Claims (8)

Parsing the bitstream of the compressed image to obtain a motion vector and a coding type for the coding unit;
A second step of obtaining a motion vector cumulative value for a first preset time for each of the plurality of image blocks constituting the compressed image;
A third step of comparing the motion vector cumulative value with a first threshold value for the plurality of image blocks;
A fourth step of marking an image block having a motion vector accumulation value exceeding the first threshold as a moving object region;
A series of coordinates of the tracking target moving object region is acquired over a series of image frames of the compressed image in relation to the moving object region (hereinafter, referred to as a 'tracking target moving object region') specified as a tracking target by a user operation. Providing a corresponding coordinate sequence to the image control apparatus;
It is configured to include,
The fifth step,
A fifth step of newly issuing and assigning a unique ID when the mobile object region is in an unassigned ID state;
A fifth step of setting a specific moving object area (hereinafter referred to as a 'tracking target moving object area') as a tracking object according to a user's operation;
A fifth step of identifying a unique ID (hereinafter, referred to as a “tracking unique ID”) assigned to the tracked moving object region;
A fifth step of sequentially calculating rectangular coordinates of the moving object region to which the tracking target unique ID value is assigned to a series of image frames constituting the compressed image and setting the coordinates of the moving object region to be tracked;
A fifth step of normalizing the series of rectangular coordinates included in the coordinate sequence of the region to be tracked corresponding to the resolution of the compressed image;
A fifth step of providing the video monitoring apparatus with the normalized coordinate sequence of the tracked moving object region;
A fifth step of revoke the assigned Unique ID when the moving object area disappears from the series of image frames;
Syntax-based object tracking method for a compressed image, characterized in that comprises a.
delete The method according to claim 1,
The rectangular coordinates include upper left coordinates (x, y), horizontal axis lengths (dx), and vertical axis lengths (dy) of a virtually formed rectangle so as to optimally include a moving object area.
The normalization process divides the upper left x coordinate and the horizontal axis length (dx) into the horizontal resolution (x_res) of the compressed image and divides the upper left y coordinate and the vertical axis length (dy) into the vertical resolution (y_res) of the compressed image. A syntax based object tracking method for a compressed image, characterized in that.
The method according to claim 1,
Performed between the fourth and fifth steps,
A step of identifying a plurality of adjacent image blocks (hereinafter, referred to as 'neighbor block') around the moving object area;
B) comparing a motion vector value obtained in the first step with respect to the plurality of neighboring blocks with a second preset threshold value;
C) additionally marking, as a moving object region, a neighboring block having a motion vector value exceeding the second threshold value as a result of the comparison in the b of the plurality of neighboring blocks;
Syntax-based object tracking method for a compressed image, characterized in that further comprises.
The method according to claim 4,
Carried out after the step c,
D) additionally marking a neighboring block having a coding type of an intra picture among the plurality of neighboring blocks as a moving object region;
Syntax-based object tracking method for a compressed image, characterized in that further comprises.
The method according to claim 5,
Carried out after the d step,
Performing an interpolation operation on the plurality of moving object areas to additionally mark up to a predetermined number of unmarked image blocks surrounded by the moving object area as a moving object area;
Syntax-based object tracking method for a compressed image, characterized in that further comprises.
The method according to claim 1,
The image block is a syntax-based object tracking method for a compressed image, characterized in that it comprises a macroblock and a subblock.
A non-transitory computer-readable recording medium having recorded thereon a program for executing a syntax-based object tracking method for a compressed image according to any one of claims 1, 3 to 7.

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