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CN107862261A - Image people counting method based on multiple dimensioned convolutional neural networks - Google Patents

Image people counting method based on multiple dimensioned convolutional neural networks Download PDF

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CN107862261A
CN107862261A CN201711014291.XA CN201711014291A CN107862261A CN 107862261 A CN107862261 A CN 107862261A CN 201711014291 A CN201711014291 A CN 201711014291A CN 107862261 A CN107862261 A CN 107862261A
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mrow
neural networks
convolutional neural
density map
image
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周圆
杨建兴
李成浩
杜晓婷
毛爱玲
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Tianjin University
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Tianjin University
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    • G06V20/50—Context or environment of the image
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    • G06V20/53—Recognition of crowd images, e.g. recognition of crowd congestion
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Abstract

The invention discloses a kind of image people counting method based on multiple dimensioned convolutional neural networks, step (1), the continuous density map label of generation, the image marked is converted into continuous estimation density map；Step (2), the accurate density map of prediction crowd is obtained using multiple dimensioned convolutional neural networks, after for convolutional neural networks, one initial parameter is set, the loss L (θ) of input picture is calculated according to the density map of reality, then the parameter of whole network is updated in Optimized Iterative each time, until penalty values converge to a less value.Compared with prior art, the present invention solves crowd's enormousness in single image and changed, on the basis of single branch convolutional neural networks, the feature of different levels network has been merged before predicted density figure is generated, the feature that different depth corresponds to different scale is extracted, greatly improves the precision of predicted density figure；The problems such as solving the dimensional variation in crowd's image and blocking.

Description

Image people counting method based on multiple dimensioned convolutional neural networks
Technical field
The present invention relates to crowd's image analysis technology field, specifically a kind of crowd based on multiple dimensioned convolutional neural networks Counting algorithm.
Background technology
It is a kind of intelligent monitoring application of the quantity of the density map calculating people by predicting crowd's image that crowd, which counts,.With The exponential increase of world population, quick urbanization promote many large-scale activities, such as sports match, Gong Zhongyou OK, the problems such as congested in traffic, causes large-scale crowd massing.So in order to preferably manage crowd and personal safety, Ren Qunhang It is significant for parser.
With the continuous popularization of deep learning algorithm, crowd's counting algorithm contrast traditional algorithm based on convolutional neural networks Substantially increase accuracy of detection.Algorithm based on convolutional neural networks is broadly divided into two kinds：A kind of is the algorithm based on recurrence, separately A kind of is the algorithm based on density map.The former is to be used as label, training convolutional nerve net with corresponding number by the use of crowd's image Network study maps to one from crowd's image to the nonlinear function of crowd's quantity, and the output of network is the number of crowd.The latter It is by the use of crowd's image and corresponding density map as label, goes training convolutional neural networks generation corresponding with input crowd's image Density map, different from the method for recurrence, the network of the algorithm based on density map is using density map as output, according to prediction Density map goes calculating crowd's quantity.But because crowd's image is deposited in monitoring camera and high-altitude shooting, shooting angle mostly In great changes, the image taken there is very big change in the size of people and yardstick.The multiple row convolution god that Zhang et al. is proposed Through network in network complexity it is very high, network parameter is very big, and three row networks need pre-training again by multiple row network output characteristic Merged, it is impossible to while hold the multi-scale information of single image.
The content of the invention
The present invention seeks to extract the feature of different depth for Tilly convolutional neural networks, different scale feature is melted Close, it is proposed that a kind of crowd density detection method based on multiple dimensioned convolutional neural networks, it is close by being predicted from crowd's image Degree figure calculates total number.
A kind of image people counting method based on multiple dimensioned convolutional neural networks of the present invention, this method include following step Suddenly：
Step 1, the continuous density map label of generation, specifically include following processing：
By density map corresponding to the good number of people Coordinate generation of handmarking, there is the graphical representation of N number of people's labeling head for such as Minor function：
In formula, δ (x-xi) it is delta function；xiRepresent the position where people's leader note point；
The image marked is converted into continuous density map, expression formula is as follows：
F (x)=H (x)*
Step 2, the accurate density map of prediction crowd is obtained using multiple dimensioned convolutional neural networks, specifically include following place Reason：
Multiple dimensioned convolutional neural networks obtain three convolutional layers by the connection in pond of the convolution of convolution-pond-again-again, from First three convolutional layer extracts the wild feature of different feeling, and these features are merged in a manner of cascading merging, then passes through Density map corresponding to two convolutional layer outputs；
The loss function L (θ) of the multiple dimensioned convolutional neural networks is calculated, expression formula is as follows：
Wherein, N be input convolutional neural networks amount of images, xiFor the i-th width input picture of convolutional neural networks, M (xi) represent the i-th width input picture standard density figure matrix；
After one initial parameter is set for convolutional neural networks, the loss L of input picture is calculated according to the density map of reality (θ), the parameter of whole network is then updated in Optimized Iterative each time, until penalty values converge to a less value.
Compared with prior art, the image people counting method of the invention based on multiple dimensioned convolutional neural networks have with Lower effect：
1st, single-row convolutional neural networks can be utilized in the case of compared with low parameter, with reference to the feature of different depth, detection The pedestrian of different scale into crowd's image；
2nd, solve crowd's enormousness change in single image, on the basis of single branch convolutional neural networks, generating The feature of different levels network has been merged before predicted density figure, has extracted the feature that different depth corresponds to different scale, greatly Improve the precision of predicted density figure；
3rd, the problems such as solving the dimensional variation in crowd's image and blocking.
Brief description of the drawings
Fig. 1 is the image people counting method overall flow schematic diagram based on multiple dimensioned convolutional neural networks of the present invention；
Fig. 2 is multiple dimensioned convolutional neural networks structure chart；
Fig. 3 is experimental result picture；It is crowd's image to scheme (a), and figure (b) is corresponding density map.
Embodiment
Embodiments of the present invention are described in further detail below in conjunction with accompanying drawing.
As shown in figure 1, a kind of crowd density detection method based on multiple dimensioned convolutional neural networks of the present invention, will be single-row Convolutional neural networks are merged in the feature of different depth, are comprised the following steps that：
Step 1, the continuous density map label of generation, the image marked is converted into continuous estimation density map, specifically Including following processing：
By density map corresponding to the good number of people Coordinate generation of handmarking, there is the graphical representation of N number of people's labeling head for such as Minor function：
In formula, δ (x-xi) it is delta function；xiRepresent the position where people's leader note point；
Estimate that density map F (x) expression formula is as follows：
F (x)=H (x)*
；
Step 2, the accurate density map of prediction crowd is obtained using multiple dimensioned convolutional neural networks：Multiple dimensioned convolutional Neural net Network obtains three convolutional layers by the connection in pond of the convolution of convolution-pond-again-again, and the not same feeling is extracted from first three convolutional layer By the feature of open country, those features are extracted multi-level feature by the convolutional layer of three different depths and formed, with adding for network Deep, the receptive field of higher convolutional layer also can be bigger, can be obtained in the feature that the convolutional layer of low level extracts more small The detailed information of object, what is obtained in high-level convolutional layer is advanced semantic feature, by these features to cascade merging Mode is merged, i.e. the superposition of characteristic pattern, then by density map corresponding to two convolutional layer outputs.The loss function of the network It is estimation density map F (xi；θ) and actual density figure M (xi) between Euclidean distance L (θ), expression is as follows：
Wherein, N be input convolutional neural networks amount of images, xiFor the i-th width input picture of convolutional neural networks, M (xi) represent the i-th width input picture accurate density map matrix；
After one initial parameter is set for convolutional neural networks, the damage of input picture is calculated according to the accurate density map of reality L (θ) is lost, the parameter of whole network is then updated in Optimized Iterative each time, until penalty values converge to a less value.
Due to camera shooting angle, different degrees of perspective distortion often occurs for crowd's image, its total body surface It is now that the area that the pedestrian nearer apart from camera occupies in the picture is larger, the area that the pedestrian away from camera occupies in the picture It is smaller.In this step, using the pedestrian of different scale in multiple dimensioned convolutional neural networks Monitoring Population image.In convolutional Neural In network, the feature of different depth represents different grades of feature in network.What convolutional neural networks were extracted in low layer is figure The profile and shape facility of picture, receptive field is relatively small, with the intensification of the network number of plies, deep layer network extraction to be image High-level semantics features, the feature of different levels in network is overlapped fusion, combined well multiple dimensioned in crowd's image Feature, it is final to produce the crowd density figure that more calculates to a nicety.
Specific embodiment is described as follows：
The present invention needs to solve the problems, such as to be " to give the frame in crowd's image or video, then estimate the figure As the density and number of regional crowd amount to "：
Known input picture is expressed as to M × N matrix：x∈Rm×n, then the actual crowd corresponding to input picture x is close Degree is expressed as：
Wherein, N represents the number in image, and x represents the position of each pixel in image, xiIt is i-th of number of people in image In position, δ (x-xi) impulse function is represented, * represents convolution operation symbol, Gδ(x) Gaussian kernel that standard deviation is δ is represented.
The target of the embodiment is one mapping function by input picture x to crowd density figure of study：
F:x→F(x)≈M(x)
Wherein, F (x) is estimation crowd density figure.
In order to learn F, it is necessary to optimize following problem：
Wherein, F (x；It is θ) estimation crowd density figure, θ is parameter to be learned.In general, F is a complex nonlinear Function.
As shown in Fig. 2 by the present invention using learning the multiple dimensioned of the nonlinear function F from crowd image to density map Convolutional neural networks.Multiple dimensioned convolutional neural networks are to be merged the feature of different depth level.By single-row convolutional Neural By a convolution, pond, second layer characteristic pattern pass through pond of a convolution to the first layer characteristic pattern of network twice, by before Two layers of obtained feature links together with the characteristic pattern that third layer convolution obtains in " passage " dimension, forms total characteristic figure Merged feature maps, then obtain density map to the end by two convolutional layers again.
The loss function of above-mentioned multiple dimensioned convolutional neural networks be estimate Euclidean between density map and actual density figure away from From：
Update the parameter L (θ) of whole network in training process in Optimized Iterative each time using gradient descent method, until Penalty values converge to a less value.
The present invention compares in three common data sets with other method, including market data set MALL, UCSD With SHANGHAITECH data sets.The evaluation criterion of experimental result uses：
Mean absolute error (MAE)：
With mean square error (MSE)：
N is picture number, ziFor number of people number actual in the i-th width image,Pass through for the i-th width image provided by the invention The number of people number of network output) carry out the accuracy of measure algorithm.On the data set of MALL markets, the of the invention and technology of existing algorithm Contrast, (wherein MD-CNN is inventive algorithm) as shown in table 1：
Table 1
On UCSD data sets, the present invention is compared with the prior art, as shown in table 2：
Table 2
Method MAE MSE
Kernelridgeregression 2.16 7.45
Ridgeregression 2.25 7.82
Gaussianprocessregression 2.24 7.97
Cumulativeattributeregression 2.07 6.86
Zhangetal. 1.60 3.31
MCNN 1.07 1.35
MDCNN(ours) 1.16 1.75
It is as shown in table 3 with the comparison of other existing algorithms on SHANGHAITECH part_B data sets：
Table 3
Method MAE MSE
LBP+RR 59.1 87.1
Zhangetal. 32 49.8
MCNN 26.4 41.3
MDCNN(ours) 22.3 39.45

Claims (1)

1. a kind of image people counting method based on multiple dimensioned convolutional neural networks, it is characterised in that this method includes following Step：
Step (1), the continuous density map label of generation, the image marked is converted into continuous estimation density map, specific bag Include following processing：
By density map corresponding to the good number of people Coordinate generation of handmarking, the graphical representation with N number of people's labeling head is following letter Number：
<mrow> <mi>H</mi> <mrow> <mo>(</mo> <mi>x</mi> <mo>)</mo> </mrow> <mo>=</mo> <munderover> <mo>&amp;Sigma;</mo> <mrow> <mi>i</mi> <mo>=</mo> <mn>1</mn> </mrow> <mi>N</mi> </munderover> <mi>&amp;delta;</mi> <mrow> <mo>(</mo> <mi>x</mi> <mo>-</mo> <msub> <mi>x</mi> <mi>i</mi> </msub> <mo>)</mo> </mrow> </mrow>
In formula, δ (x-xi) it is delta function；xiRepresent the position where people's leader note point；
Estimate that density map F (x) expression formula is as follows：
F (x)=H (x)*
；
Step (2), the accurate density map of prediction crowd is obtained using multiple dimensioned convolutional neural networks, specifically include following processing：
Multiple dimensioned convolutional neural networks obtain three convolutional layers by the connection in pond of the convolution of convolution-pond-again-again, from first three Individual convolutional layer extracts the wild feature of different feeling, and these features are merged in a manner of cascading merging, then by two Density map corresponding to convolutional layer output；
The loss function L (θ) of the multiple dimensioned convolutional neural networks is calculated, expression formula is as follows：
<mrow> <mi>L</mi> <mrow> <mo>(</mo> <mi>&amp;theta;</mi> <mo>)</mo> </mrow> <mo>=</mo> <mfrac> <mi>I</mi> <mrow> <mn>2</mn> <mi>N</mi> </mrow> </mfrac> <munderover> <mo>&amp;Sigma;</mo> <mrow> <mi>i</mi> <mo>=</mo> <mn>1</mn> </mrow> <mi>N</mi> </munderover> <mo>|</mo> <mo>|</mo> <mi>F</mi> <mrow> <mo>(</mo> <msub> <mi>x</mi> <mi>i</mi> </msub> <mo>,</mo> <mi>&amp;theta;</mi> <mo>)</mo> </mrow> <mo>-</mo> <mi>M</mi> <mrow> <mo>(</mo> <msub> <mi>x</mi> <mi>i</mi> </msub> <mo>)</mo> </mrow> <mo>|</mo> <msubsup> <mo>|</mo> <mn>2</mn> <mn>2</mn> </msubsup> </mrow>
Wherein, N be input convolutional neural networks amount of images, xiFor the i-th width input picture of convolutional neural networks, M (xi) table Show the accurate density map matrix of the i-th width input picture；
After one initial parameter is set for convolutional neural networks, the loss L of input picture is calculated according to the accurate density map of reality (θ), the parameter of whole network is then updated in Optimized Iterative each time, until penalty values converge to a less value.
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Application publication date: 20180330