JP2020141221A - Determination device, determination method, and program - Google Patents

Abstract

To provide a technique for accurately detecting a predetermined event based on a plurality of pieces of modality information.SOLUTION: A determination device includes: a voice feature extraction unit for extracting voice features from voice data; a video feature extraction unit for extracting video features from video data; a sensor feature extraction unit for extracting sensor features from sensor data; and a determination unit for determining presence or absence of a predetermined event based on the voice features, the video features, and the sensor features.SELECTED DRAWING: Figure 1

Description

The present invention relates to a technique for detecting a predetermined event by using a plurality of modality information.

In recent years, drive recorders have become widespread. The video of the drive recorder is used, for example, for an investigation in the event of a traffic accident and for determining the error rate. In addition, there are an increasing number of cases in which drive recorder images are used for safe driving guidance such as improving the driver's awareness of safe driving and promoting awareness of dangerous driving.

In order to utilize the drive recorder video for safe driving guidance, it is necessary to extract and discriminate dangerous driving scenes from a large number of recorded video of the drive recorder. However, there is a problem that the discrimination work requires a lot of time, personnel, labor and attention. Therefore, it is required to realize a technique for automatically discriminating dangerous driving scenes.

Japanese Unexamined Patent Publication No. 2018-126374

By learning the deep neural network from the video and the correct label, it is conceivable that the deep neural network automatically discriminates the dangerous driving scene.

However, in order to automatically determine a dangerous driving scene, it is necessary to adopt a structure of a deep neural network that is complicated and has a deep layer, and the amount of calculation becomes very large and the amount of memory required becomes also very large. .. Also, the time required for learning can be enormous. Therefore, it is not realistic to learn a deep neural network by using a single modality information such as a video to automatically determine a dangerous driving scene. Further, although there is a conventional method of automatically discriminating a dangerous driving scene using video data and sensor data, the accuracy is not sufficient.

It should be noted that the above-mentioned problems are not limited to the detection of dangerous driving scenes, but may occur in the detection of predetermined events in various fields.

The present invention has been made in view of the above points, and an object of the present invention is to provide a technique for accurately detecting a predetermined event based on a plurality of modality information.

According to the disclosed technology, a voice feature extraction unit that extracts voice features from voice data and
A video feature extraction unit that extracts video features from video data,
A sensor feature extraction unit that extracts sensor features from sensor data,
A determination device including a determination unit for determining the presence or absence of a predetermined event based on the audio feature amount, the video feature amount, and the sensor feature amount is provided.

According to the disclosed technology, it is possible to provide a technology for accurately detecting a predetermined event based on a plurality of modality information.

It is a functional block diagram of the determination apparatus in embodiment of this invention. It is a figure which shows the hardware configuration example of the determination apparatus. It is a functional block diagram of the determination apparatus in embodiment of this invention. It is a functional block diagram of the determination apparatus in embodiment of this invention. It is a functional block diagram of the determination apparatus in embodiment of this invention. It is a figure for demonstrating the evaluation result. It is a figure which shows the mixing matrix in the prior art. It is a figure which shows the mixing matrix in the technique which concerns on this invention.

Hereinafter, embodiments of the present invention (the present embodiments) will be described with reference to the drawings. The embodiments described below are merely examples, and the embodiments to which the present invention is applied are not limited to the following embodiments.

In the present embodiment, an example of determining the presence or absence of dangerous driving based on video data, audio data, and sensor data acquired from an automobile will be described, but the application of the present invention is to determine the presence or absence of dangerous driving. The present invention is not limited, and the present invention is applicable to various other fields. For example, the present invention can be applied to the stage of making mash in the sake manufacturing process.

(Device configuration)
FIG. 1 shows an example of a functional configuration of the determination device 100 according to the embodiment of the present invention. As shown in FIG. 1, the determination device 100 includes a voice preprocessing unit 101, a video preprocessing unit 102, a sensor preprocessing unit 103, a voice feature amount extraction network unit 104, a video feature amount extraction network unit 105, and a sensor feature amount extraction. Network unit 106, audio feature compression unit 107, video feature compression unit 108, sensor feature compression unit 109, video / sensor feature coupling unit 110, time series feature modeling unit 111, video / sensor / audio feature coupling unit 112, non-time series It has a feature modeling unit 113 and an output unit 114. The outline of the function of each part is as follows.

The voice preprocessing unit 101 executes preprocessing for extracting, for example, Mel-Frequency Cepstrum Coefficients (MFCC) from the input voice data.

The video preprocessing unit 102 performs a process of dividing the input video (moving image) data into a predetermined number of images. Note that the video data may be used as it is for input without performing the process of dividing each of a predetermined number of images.

The sensor preprocessing unit 103 normalizes the sensor data such as acceleration and velocity so that the values are, for example, an average of 0 and a standard deviation of 1.

The audio feature amount extraction network unit 104, the video feature amount extraction network unit 105, and the sensor feature amount extraction network unit 106 are deep neural networks (DNNs), respectively. For example, CNN (Convolutional Neural Network) can be used as these feature quantity extraction network units 104 to 106. A CNN is a neural network having an output layer, a convolution layer, a pooling layer, a fully connected layer, and an output layer.

The audio feature compression unit 107, the video feature compression unit 108, and the sensor feature compression unit 109 are, for example, neural networks in which the number of dimensions of the output is smaller than the number of dimensions of the input. Further, the function of principal component analysis (PCA) or the function of non-negative matrix factorization (NMF) may be used as the audio feature compression unit 107, the video feature compression unit 108, and the sensor feature compression unit 109, respectively.

The video / sensor feature coupling unit 110 combines the feature amount output from the video feature compression unit 108 with the feature amount output from the sensor feature compression unit 109.

The time-series feature modeling unit 111 extracts features of time-series data such that the data at a certain time is influenced by the data at a time earlier than that. The time-series feature modeling unit 111 is, for example, a neural network composed of RNN (Recurrent Neural Network), RSTM (Long Short-Term Memory), and the like. The feature amount output from the time-series feature modeling unit 111 is referred to as a time-series feature amount.

The video / sensor / audio feature coupling unit 112 combines the feature quantity output from the audio feature compression unit 107 with the time series feature quantity output from the time series feature modeling unit 111.

The non-time series feature modeling unit 113 is a deep neural network or the like that does not have the property of extracting time series features. However, a deep neural network having the property of extracting time series features may be used here.

The output unit 114 outputs a target determination result. In the case of this embodiment, for example, a label indicating whether or not the driving is dangerous is output.

The "audio preprocessing unit 101 + audio feature extraction network unit 104 + audio feature compression unit 107" is called an audio feature extraction unit, and the "video preprocessing unit 102 + video feature extraction network unit 105 + video feature compression unit 108" is called a video. It may be called a feature extraction unit, and "sensor preprocessing unit 103 + sensor feature amount extraction network unit 106 + sensor feature compression unit 109" may be called a sensor feature extraction unit.

Further, "video / sensor feature coupling unit 110 + time series feature modeling unit 111 + video / sensor / audio feature coupling unit 112 + non-time series feature modeling unit 113" may be referred to as a determination unit.

(Hardware configuration example)
The determination device 100 can be realized, for example, by causing a computer to execute a program describing the processing contents described in the present embodiment. The points that can be realized by a computer and a program are the same for the determination devices described with reference to FIGS. 3 to 5 described later.

That is, the determination device 100 can be realized by executing a program corresponding to the processing executed by the determination device 100 by using the hardware resources such as the CPU and the memory built in the computer. The above program can be recorded on a computer-readable recording medium (portable memory, etc.), stored, and distributed. It is also possible to provide the above program through a network such as the Internet or e-mail.

FIG. 2 is a diagram showing a hardware configuration example of the computer according to the present embodiment. The computer of FIG. 2 has a drive device 1000, an auxiliary storage device 1002, a memory device 1003, a CPU 1004, an interface device 1005, a display device 1006, an input device 1007, and the like, which are connected to each other by bus B, respectively.

The program that realizes the processing in the computer is provided by, for example, a recording medium 1001 such as a CD-ROM or a memory card. When the recording medium 1001 storing the program is set in the drive device 1000, the program is installed in the auxiliary storage device 1002 from the recording medium 1001 via the drive device 1000. However, the program does not necessarily have to be installed from the recording medium 1001, and may be downloaded from another computer via the network. The auxiliary storage device 1002 stores the installed program and also stores necessary files, data, and the like.

The memory device 1003 reads and stores the program from the auxiliary storage device 1002 when the program is instructed to start. The CPU 1004 realizes the function related to the determination device 100 according to the program stored in the memory device 1003. The interface device 1005 is used as an interface for connecting to a network. The display device 1006 displays a programmatic GUI (Graphical User Interface) or the like. The input device 1007 is composed of a keyboard, a mouse, buttons, a touch panel, and the like, and is used for inputting various operation instructions.

(Operation of determination device 100)
Here, as an example of the operation of the determination device 100 shown in FIG. 1, an operation of determining whether or not the vehicle is dangerous driving based on the audio data, video data, and sensor data collected by the microphone, camera, and sensor mounted on the automobile. Will be described. The sensor data here is, for example, the speed and acceleration of the vehicle. The "audio data, video data, and sensor data collected by the microphone, camera, and sensor" are, for example, data collected by a drive recorder.

<Learning phase>
Prepare a large number (“audio data, video data, sensor data” and correct label) collected from a large number of automobiles. Here, one "audio data, video data, sensor data" may be a file in which data for a predetermined time length is stored. The correct answer label may be attached to each file, or may be attached to the data in a finer time unit in the file.

The "audio data, video data, sensor data" are sequentially input to the determination device 100, the output value from the output unit 114 is compared with the correct answer label, and the parameters of each part in the determination device 100 so as to reduce the error ( The optimum parameter is obtained by modifying the weight).

The determination device 100 in which the optimum parameters are set determines the dangerous driving in the determination phase. The existing technology can be used for modifying the parameters.

Further, in the present embodiment, there are three types of correct label, "accident", "near miss", and "normal (No-Near-Miss)". However, this is just an example.

<Judgment phase>
Next, the operation of the determination device 100 in the determination phase will be described. The operation of the determination device 100 from the input to the output is the same in the learning phase and the determination phase. However, in the determination phase, since the optimum parameters are set, there is a high possibility that the output unit 114 will give a correct answer.

In the determination phase, the data to be determined is prepared. The data is data in the same format as the data given in the learning phase and does not have a correct answer label. Further, in the determination phase, "audio data, video data, sensor data" acquired in real time from the vehicle to be determined may be used as input data.

As shown in FIG. 1, audio data, video data, and sensor data are sequentially input to the audio preprocessing unit 101, the video preprocessing unit 102, and the sensor preprocessing unit 103, respectively.

The voice preprocessing unit 101 extracts Mel-Frequency Cepstrum Coefficients (MFCC) from the input voice data, and inputs the Mel-Frequency Cepstrum Coefficients to the voice feature extraction network unit 104.

The video preprocessing unit 102 performs a process of dividing the input video data (data in which images (frames) are continuously arranged) into a predetermined number of images, and each divided data (a plurality of image data). ) Is input to the video feature amount extraction network unit 105. The sensor preprocessing unit 103 normalizes the sensor data, and inputs the normalized data to the sensor feature amount extraction network unit 106.

Regarding the data input to each feature amount extraction network unit, regarding the video data and the sensor data, assuming that the time advances, for example, time 1, time 2, and time 3, first, the video data 1 and the sensor at time 1 Data 1 is input, then video data 2 and sensor data 2 at time 2 are input, then video data 3 and sensor data 3 at time 3 are input, and synchronized data such as ... Entered. The video data at one time here is a set of a predetermined number of images. The sensor data is the speed and acceleration obtained at that time.

The audio data is input here in a larger time unit, not in the time unit corresponding to the video data and the sensor data. For example, voice data 1 at times 1 to 10, voice data 2 at times 11 to 20, and so on are input. Therefore, here, it is assumed that time-series features such as video data and sensor data cannot be extracted for audio data.

The audio feature amount extraction network unit 104, the video feature amount extraction network unit 105, and the sensor feature amount extraction network unit 106 each output the feature amount of the input data.

The audio feature amount extraction network unit 104, the video feature amount extraction network unit 105, and the sensor feature amount extraction network unit 106 each input a vector X of a certain dimension number as input data, and a vector Y of a certain dimension number as output data. Is output. This vector Y is the above-mentioned feature quantity.

The audio feature compression unit 107, the video feature compression unit 108, and the sensor feature compression unit 109 each input the feature amount (vector) output from the feature amount extraction network unit in the preceding stage, compress the feature amount, and then compress the feature amount. Outputs a vector with a number of dimensions smaller than the number of dimensions of the input vector.

The video / sensor feature coupling unit 110 combines the feature amount output from the video feature compression unit 108 with the feature amount output from the sensor feature compression unit 109. For example, the feature amount output from the video feature compression unit 108 is a vector (a1, a2, a3), and the feature amount output from the sensor feature compression unit 109 is a vector (b1, b2, b3). Then, the video / sensor feature coupling unit 110 outputs vectors (a1, a2, a3, b1, b2, b3).

The combined feature amount output from the video / sensor feature coupling unit 110 is input to the time series feature modeling unit 111, and the time series feature modeling unit 111 outputs the time series feature amount. The time-series feature modeling unit 111 also basically takes a vector X of a certain dimension as an input and outputs a vector Y of a certain dimension as output data.

The video / sensor / audio feature coupling unit 112 combines the feature amount output from the audio feature compression unit 107 and the feature amount output from the time series feature modeling unit 111. For example, the feature amount output from the voice feature compression unit 107 is a vector (c1, c2, c3), and the feature amount output from the time series feature modeling unit 111 is a vector (d1, d2, d3). If so, the video / sensor / audio feature coupling unit 110 outputs a vector (c1, c2, c3, d1, d2, d3).

The non-time series feature modeling unit 113 extracts a feature amount to be a determination result from the feature amount input from the video / sensor / audio feature combination unit 110, and gives this to the output unit 114. The output unit outputs the determination result. The non-time series feature modeling unit 113 also basically takes a vector X of a certain dimension as an input and outputs a vector Y of a certain dimension as output data.

The non-time series feature modeling unit 113 (and the output unit 114) outputs the determination result (determined label) for each data input of a predetermined time length (for example, the above-mentioned input file unit). It may be configured, or it may be configured to output at each time (the unit of time 1, time 2, ... described above).

(Other examples)
In the above example, for video data and sensor data, synchronized data can be given to the feature amount extraction network unit, and for audio data, data synchronized with the video data and sensor data is given to the feature amount extraction network unit. Therefore, as shown in FIG. 1, the audio feature amount is not passed through the time-series feature modeling unit 111, but in the video / sensor / audio feature coupling unit 112, the time-series feature amount of the video / sensor data. It is supposed to be combined with.

Here, as for the audio data, if the data synchronized with the video data and the sensor data can be given to the feature amount extraction network unit, the determination device may be the determination device 200 shown in FIG.

In the configuration shown in FIG. 3, in the audio / video / sensor feature coupling unit 210, the audio data feature amount, the video data feature amount, and the sensor data feature amount are combined, and the combined feature amount is the time series feature modeling unit. It is output to 211, and the time-series feature modeling unit 211 extracts the time-series feature amount for the combined feature amount and outputs it to the output unit 212.

Further, for audio data and sensor data, synchronized data can be given to the feature amount extraction network unit, and for video data, audio data and data synchronized with sensor data cannot be given to the feature amount extraction network unit. In some cases, the configuration shown in FIG. 4 may be used. In this case, the video feature amount is combined with the time-series feature amount of the voice / sensor data in the video / sensor / voice feature coupling unit 112 without passing through the time-series feature modeling unit 111.

Further, for audio data and video data, synchronized data can be given to the feature amount extraction network unit, and for sensor data, data synchronized with the audio data and video data cannot be given to the feature amount extraction network unit. In some cases, the configuration shown in FIG. 5 may be used. In this case, the feature amount of the sensor data is combined with the time-series feature amount of the audio / video data in the video / sensor / audio feature coupling unit 112 without passing through the time-series feature modeling unit 111.

(Evaluation results)
As shown in FIG. 1, the evaluation result of the technique according to the present invention in the configuration considering the non-time series of voice will be described.

FIG. 6 shows the evaluation indexes (Precision, Recall, F1-Score) of the conventional method using video data and sensor data (upper side of FIG. 6) and the technique according to the present invention (lower side of FIG. 6). The result is shown. It can be seen that the technique according to the present invention gives better results than the conventional method in all the indexes.

FIG. 7 shows a mixing matrix with respect to the values calculated by the conventional method. For example, paying attention to the line of Accident, it is determined that the number of data of the correct label (Acident) is 36, of which 15, the number of which the determination result of Accident is obtained is Near Miss. It is shown that the number of results obtained is 13 and the number of determination results of No Near Miss is 8. The same is true for the other lines.

FIG. 8 shows a mixing matrix when the technique according to the present invention is used. The way of reading the figure is the same as that of FIG.

As shown in FIGS. 7 and 8, this book is compared with the conventional method (FIG. 7) in which there are many misrecognitions (thick frame portion in the figure) of “accident” and “near-miss”. It can be seen that this is improved in the technique according to the invention (FIG. 8). For example, in the case of an accident caused by a collision at a low speed, environmental sounds such as collision sounds generated at the time of an accident and human voice can be judged with accuracy for events that are difficult to distinguish from images and sensors alone. It is thought that it contributes to the improvement.

(Summary of embodiments, effects)
As described above, in the present embodiment, the dangerous driving scene can be accurately discriminated by using the modality information of all the video, audio, and sensor in an integrated manner.

Further, in the present embodiment, instead of taking a complicated and computationally heavy neural network structure by using a single modality information, a plurality of modality informations are used in an integrated manner, so that the amount of calculation is compact. Discrimination accuracy can be guaranteed by the structure of a relatively lightweight neural network.

The heavy neural network is, for example, a network in which dozens of layers of each layer of the neural network (for example, a convolution layer, a fully connected layer, etc.) are stacked, and typical network architectures include VGG16, VGG19, ResNet-50, and the like. There is. Further, the lightweight neural network is, for example, a network in which each layer of the neural network (for example, a convolution layer, a fully connected layer, etc.) is simply stacked in several layers.

That is, by using a plurality of modal information, classification accuracy equal to or higher than that of a heavy neural network configuration using only one modality information is realized only by a combination of lightweight neural network configurations. In addition, by using multiple modal information, high speed, memory saving, and ease of learning are realized compared to a heavy neural network configuration that uses only one modality information by combining only lightweight neural network configurations. ing.

At least, the determination device, determination method, and program in each of the following items are disclosed in the present specification.
(Section 1)
A voice feature extraction unit that extracts voice features from voice data,
A video feature extraction unit that extracts video features from video data,
A sensor feature extraction unit that extracts sensor features from sensor data,
A determination device including a determination unit that determines the presence or absence of a predetermined event based on the audio feature amount, the video feature amount, and the sensor feature amount.
(Section 2)
The determination unit
A first feature coupling portion that combines two features of the audio feature amount, the video feature amount, and the sensor feature amount, and
A time-series feature extraction unit that extracts a time-series feature amount from the feature amount combined by the first feature combination unit, and a time-series feature extraction unit.
A second feature that combines the time-series feature amount output from the time-series feature extraction unit with the audio feature amount, the video feature amount, and the feature amount other than the two feature amounts of the sensor feature amount. At the joint,
The determination device according to item 1, further comprising a feature extraction unit that extracts a feature amount to be a determination result from the feature amount combined by the second feature combination unit.
(Section 3)
The determination unit
A coupling portion that combines the audio feature amount, the video feature amount, and the sensor feature amount,
The determination device according to item 1, further comprising a time-series feature extraction unit that extracts a time-series feature amount that is a determination result from the feature amounts combined by the coupling unit.
(Section 4)
It is a judgment method executed by the judgment device.
A voice feature extraction step that extracts voice features from voice data,
A video feature extraction step that extracts video features from video data,
A sensor feature extraction step that extracts sensor features from sensor data,
A determination method including a determination step for determining the presence or absence of a predetermined event based on the audio feature amount, the video feature amount, and the sensor feature amount.
(Section 5)
A program for causing a computer to function as each part in the determination device according to any one of the first to third paragraphs.

Although the present embodiment has been described above, the present invention is not limited to such a specific embodiment, and various modifications and changes can be made within the scope of the gist of the present invention described in the claims. It is possible.

100, 200 Judgment device 101, 201 Audio preprocessing unit 102, 202 Video preprocessing unit 103, 203 Sensor preprocessing unit 104, 204 Audio feature amount extraction network unit 105, 205 Image feature amount extraction network unit 106, 206 Sensor feature amount Extraction network unit 107, 207 Audio feature compression unit 108, 208 Video feature compression unit 109, 209 Sensor feature compression unit 111, 211 Time series feature modeling unit 112, 210 Video / sensor / audio feature coupling unit 114, 212 Output unit 1000 drive Device 1002 Auxiliary storage device 1003 Memory device 1004 CPU
1005 Interface device 1006 Display device 1007 Input device

Claims (5)

A voice feature extraction unit that extracts voice features from voice data,
A video feature extraction unit that extracts video features from video data,
A sensor feature extraction unit that extracts sensor features from sensor data,
A determination device including a determination unit that determines the presence or absence of a predetermined event based on the audio feature amount, the video feature amount, and the sensor feature amount. The determination unit
A first feature coupling portion that combines two features of the audio feature amount, the video feature amount, and the sensor feature amount, and
A time-series feature extraction unit that extracts a time-series feature amount from the feature amount combined by the first feature combination unit, and a time-series feature extraction unit.
A second feature that combines the time-series feature amount output from the time-series feature extraction unit with the audio feature amount, the video feature amount, and the feature amount other than the two feature amounts of the sensor feature amount. At the joint,
The determination device according to claim 1, further comprising a feature extraction unit that extracts a feature amount to be a determination result from the feature amount combined by the second feature combination unit. The determination unit
A coupling portion that combines the audio feature amount, the video feature amount, and the sensor feature amount,
The determination device according to claim 1, further comprising a time-series feature extraction unit that extracts a time-series feature amount that is a determination result from the feature amounts combined by the coupling unit. It is a judgment method executed by the judgment device.
A voice feature extraction step that extracts voice features from voice data,
A video feature extraction step that extracts video features from video data,
A sensor feature extraction step that extracts sensor features from sensor data,
A determination method including a determination step for determining the presence or absence of a predetermined event based on the audio feature amount, the video feature amount, and the sensor feature amount. A program for causing a computer to function as each part in the determination device according to any one of claims 1 to 3.