CN106815563B - Human body apparent structure-based crowd quantity prediction method - Google Patents

Human body apparent structure-based crowd quantity prediction method Download PDF

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CN106815563B
CN106815563B CN201611225785.8A CN201611225785A CN106815563B CN 106815563 B CN106815563 B CN 106815563B CN 201611225785 A CN201611225785 A CN 201611225785A CN 106815563 B CN106815563 B CN 106815563B
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CN106815563A (en
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黄思羽
张仲非
李玺
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Zhejiang University ZJU
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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
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    • G06V20/00—Scenes; Scene-specific elements
    • G06V20/50—Context or environment of the image
    • G06V20/52—Surveillance or monitoring of activities, e.g. for recognising suspicious objects
    • G06V20/53—Recognition of crowd images, e.g. recognition of crowd congestion
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F18/00—Pattern recognition
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    • G06F18/21—Design or setup of recognition systems or techniques; Extraction of features in feature space; Blind source separation
    • G06F18/214—Generating training patterns; Bootstrap methods, e.g. bagging or boosting
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    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
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    • G06N3/084—Backpropagation, e.g. using gradient descent
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V10/00—Arrangements for image or video recognition or understanding
    • G06V10/20—Image preprocessing
    • G06V10/26—Segmentation of patterns in the image field; Cutting or merging of image elements to establish the pattern region, e.g. clustering-based techniques; Detection of occlusion
    • G06V10/267—Segmentation of patterns in the image field; Cutting or merging of image elements to establish the pattern region, e.g. clustering-based techniques; Detection of occlusion by performing operations on regions, e.g. growing, shrinking or watersheds

Abstract

The invention discloses a crowd quantity prediction method based on a human body apparent structure, which is used for predicting the crowd quantity in a given scene image. The method specifically comprises the following steps: acquiring a monitoring image data set used for training a crowd quantity prediction model, and defining an algorithm target; modeling an apparent semantic structure of a pedestrian body in the monitoring image data set, and performing combined modeling on density distribution and body shape of the pedestrian; establishing a prediction model of the crowd quantity according to the modeling result in the step S2; and predicting the number of people in the scene image by using the prediction model. The method is suitable for predicting the number of people in a real video monitoring scene, and has better effect and robustness in the face of various complex conditions.

Description

Human body apparent structure-based crowd quantity prediction method
Technical Field
The invention belongs to the field of computer vision, and particularly relates to a crowd quantity prediction method based on a human body apparent structure.
Background
Since the end of the 20 th century, with the development of computer vision, intelligent video surveillance technology has gained widespread attention and research. People counting is one of the important and challenging tasks, with the goal of accurately predicting the number of pedestrians in high-density people images. Three key factors of the crowd counting task are the pedestrian, the head and their contextual structure. When people count the number of people, the semantic structures of different parts of the bodies of the people are used as clues to accurately judge the positions of the people. Therefore, accurately predicting the number of people requires analysis of the semantic structure of the pedestrian's body.
Existing population counting methods generally include the following three categories: 1. people counting based on pedestrian detectors. Such methods utilize various pedestrian detectors to match each pedestrian in the image; 2. population counts based on global regression. The method mainly models the mapping between the crowd image and the crowd quantity; 3. population counts based on density estimates. The method models the density distribution of the crowd and predicts the crowd quantity through the density distribution. Existing methods model the entire body of the pedestrian as a whole, or only the head of the pedestrian. They ignore rich semantic structural information of the pedestrian body parts, and the performance of the crowd counting algorithm can be improved by utilizing the structural information.
Disclosure of Invention
In order to solve the above problems, the present invention provides a method for predicting the number of people in a given scene image based on the apparent structure of human body. The method carries out semantic modeling on the body apparent structure and density distribution information of the pedestrian based on the deep neural network, predicts the accurate crowd quantity according to the modeling result, and can better adapt to the complex situation in the real video monitoring scene.
In order to achieve the purpose, the technical scheme of the invention is as follows:
a crowd quantity prediction method based on human body apparent structure comprises the following steps:
s1, acquiring a monitoring image data set used for training a crowd quantity prediction model, and defining an algorithm target;
s2, modeling the apparent semantic structure of the pedestrian body in the monitoring image data set, and performing combined modeling on the density distribution and the body shape of the pedestrian;
s3, establishing a prediction model of the crowd quantity according to the modeling result in the step S2;
and S4, predicting the number of people in the scene image by using the prediction model.
Further, in step S1, the monitoring image data set for training the population quantity prediction model includes a scene image
Figure BDA0001192992060000021
Artificially labeled head position P of pedestriantrainAnd scene depth map
Figure BDA0001192992060000022
The algorithm targets are defined as: predicting a scene image
Figure BDA0001192992060000023
Number of pedestrians
Figure BDA0001192992060000024
Further, in step S2, the modeling the apparent semantic structure of the pedestrian body specifically includes:
s21, collecting head positions P of all pedestrians according to the monitoring image datatrainAnd their respective scene depth values
Figure BDA0001192992060000025
Determining the position and size of each pedestrian image bounding box from the set of scene images
Figure BDA0001192992060000026
Middle cutting to obtain pedestrian image Itrain；
S22, displaying the pedestrian image ItrainRespectively inputting a single pedestrian semantic segmentation system for semantic segmentation;
s23, for each scene image
Figure BDA0001192992060000027
Restoring the segmentation results of all the pedestrians according to the original size and position to obtain a scene image
Figure BDA0001192992060000028
Semantic structure diagram of crowd
Figure BDA0001192992060000029
Figure BDA00011929920600000210
Reflecting scene images
Figure BDA00011929920600000211
Semantic structure information of body parts of all pedestrians.
Further, in step S2, the joint modeling of the density distribution and the body shape of the pedestrian specifically includes:
s24, aiming at scene image
Figure BDA00011929920600000212
Performing combined modeling on the density distribution and the body shape of the pedestrians to obtain a structured crowd density map
Figure BDA00011929920600000213
Figure BDA00011929920600000214
Wherein p is
Figure BDA00011929920600000215
The position of the upper pixel in the image,
Figure BDA00011929920600000216
is a two-dimensional gaussian kernel to approximate the shape of a human head,
Figure BDA00011929920600000217
is a two-dimensional gaussian kernel to approximate the shape of the human body,
Figure BDA00011929920600000218
and
Figure BDA00011929920600000219
the central positions of the ith individual's head and body respectively,
Figure BDA00011929920600000220
is taken from Ptrain，
Figure BDA00011929920600000221
By
Figure BDA00011929920600000222
And scene depth value
Figure BDA00011929920600000223
Estimate to obtainhAnd σbAre respectively
Figure BDA00011929920600000224
And
Figure BDA00011929920600000225
of (a) each of which consists of
Figure BDA00011929920600000226
And
Figure BDA00011929920600000227
the result of the estimation is that,
Figure BDA00011929920600000228
semantic structure diagram of crowd
Figure BDA00011929920600000229
The binary image is obtained by the binary image,
Figure BDA0001192992060000031
is the number of pedestrians in the scene, and Z is a normalization factor for each pedestrian in the scene
Figure BDA0001192992060000032
Sum of Density 1, structured population Density map
Figure BDA0001192992060000033
Reflecting scene images
Figure BDA0001192992060000034
The density distribution and body shape information of all pedestrians.
Further, in step S3, the establishing a prediction model of the population specifically includes:
s31, establishing a deep convolution neural network, wherein the input of the neural network is a scene image
Figure BDA0001192992060000035
Output is corresponding to
Figure BDA0001192992060000036
Semantic structure diagram of crowd
Figure BDA0001192992060000037
Structured population density map
Figure BDA0001192992060000038
And
Figure BDA0001192992060000039
number of pedestrians
Figure BDA00011929920600000310
Thus, the structure of the neural network can be represented as a map
Figure BDA00011929920600000311
S32, child mapping
Figure BDA00011929920600000312
Using a soft maximum (Softmax) loss function, expressed as
Figure BDA00011929920600000313
Wherein
Figure BDA00011929920600000314
Is one of the outputs of the neural network,
Figure BDA00011929920600000315
to represent
Figure BDA00011929920600000316
The middle pixel position (h, w) and the value of channel i,
Figure BDA00011929920600000317
generated by the method described in step S23,
Figure BDA00011929920600000318
to represent
Figure BDA00011929920600000319
The value of the middle pixel position (h, w);
s33, child mapping
Figure BDA00011929920600000320
Using Euclidean loss function, expressed as
Figure BDA00011929920600000321
Wherein
Figure BDA00011929920600000322
Is one of the outputs of the neural network,
Figure BDA00011929920600000323
generated by the method of step S24;
s34, child mapping
Figure BDA00011929920600000324
Using Euclidean loss function, expressed as
Figure BDA00011929920600000325
Wherein
Figure BDA00011929920600000326
Is one of the outputs of the neural network,
Figure BDA00011929920600000327
is the number of people manually labeled;
s35 loss function of the whole neural network
L＝Lc+λdLd+λbLbFormula (5)
The entire neural network is trained using a stochastic gradient descent and back propagation algorithm under a loss function L.
Further, in step S4, the predicting the number of people in the scene image includes: image of a scene to be predicted
Figure BDA00011929920600000328
Inputting the trained neural network, and outputting the population number
Figure BDA00011929920600000329
I.e. the result of the prediction of the number of the crowd.
Compared with the existing crowd quantity prediction method, the crowd quantity prediction method based on the human body apparent structure has the following beneficial effects:
firstly, the method for predicting the number of the crowd discovers the semantic attribute of the crowd counting problem, defines and models three key factors of the problem: body, head and their contextual structure. This assumption is more adaptive to the complexity in the actual scene.
Secondly, the crowd quantity prediction method establishes a crowd quantity prediction model based on the deep convolutional neural network. The deep convolutional neural network can better express visual features, in addition, visual feature extraction, pedestrian semantic modeling and crowd quantity regression are unified in the same frame, and the final effect of the method is improved.
The crowd quantity prediction method based on the human body apparent structure has good application value in an intelligent video monitoring analysis system, and can effectively improve the efficiency and accuracy of crowd quantity prediction. For example, in the application scene of public safety, the crowd quantity prediction method can quickly and accurately predict the pedestrian quantity in the shooting area of the monitoring camera, and provides decision basis for daily operation and emergency treatment in public places.
Drawings
Fig. 1 is a schematic flow chart of a human body apparent structure-based crowd quantity prediction method of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is described in further detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.
On the contrary, the invention is intended to cover alternatives, modifications, equivalents and alternatives which may be included within the spirit and scope of the invention as defined by the appended claims. Furthermore, in the following detailed description of the present invention, certain specific details are set forth in order to provide a better understanding of the present invention. It will be apparent to one skilled in the art that the present invention may be practiced without these specific details.
Referring to fig. 1, in a preferred embodiment of the present invention, a method for predicting the number of people based on the apparent structure of human body comprises the following steps:
first, a monitoring image dataset for training a population quantity prediction model is obtained. Wherein the monitoring image data set used for training the crowd quantity prediction model comprises a scene image
Figure BDA0001192992060000041
Artificially labeled head position P of pedestriantrainAnd scene depth map
Figure BDA0001192992060000042
The algorithm targets are defined as: predicting a scene image
Figure BDA0001192992060000043
Number of pedestrians
Figure BDA0001192992060000044
Secondly, the density distribution and body shape of the pedestrian in the obtained monitoring image dataset are jointly modeled. Specifically, the method comprises the following steps:
first, according to the head positions P of all the pedestrians in the monitored image data settrainAnd their respective scene depth values
Figure BDA0001192992060000051
Determining the position and size of each pedestrian image bounding box from the set of scene images
Figure BDA0001192992060000052
Middle cutting to obtain pedestrian image Itrain；
Second, the pedestrian image ItrainRespectively inputting a single pedestrian semantic segmentation system for semantic segmentation;
third, for each scene image
Figure BDA0001192992060000053
Restoring the segmentation results of all the pedestrians according to the original size and position to obtain a scene image
Figure BDA0001192992060000054
Semantic structure diagram of crowd
Figure BDA0001192992060000055
Figure BDA0001192992060000056
Reflecting scene images
Figure BDA0001192992060000057
Semantic structure information of body parts of all pedestrians.
Next, the density distribution and the body shape of the pedestrian are jointly modeled. For scene image
Figure BDA0001192992060000058
Performing combined modeling on the density distribution and the body shape of the pedestrians to obtain a structured crowd density map
Figure BDA0001192992060000059
Figure BDA00011929920600000510
Wherein p is
Figure BDA00011929920600000511
The position of the upper pixel in the image,
Figure BDA00011929920600000512
is a two-dimensional gaussian kernel to approximate the shape of a human head,
Figure BDA00011929920600000513
is a two-dimensional gaussian kernel to approximate the shape of the human body.
Figure BDA00011929920600000514
And
Figure BDA00011929920600000515
the central positions of the ith individual's head and body respectively,
Figure BDA00011929920600000516
is taken from Ptrain，
Figure BDA00011929920600000517
By
Figure BDA00011929920600000518
And scene depth value
Figure BDA00011929920600000519
And (6) estimating. SigmahAnd σbAre respectively
Figure BDA00011929920600000520
And
Figure BDA00011929920600000521
of (a) each of which consists of
Figure BDA00011929920600000522
And
Figure BDA00011929920600000523
and (4) estimating to obtain.
Figure BDA00011929920600000524
Semantic structure diagram of crowd
Figure BDA00011929920600000525
And (4) carrying out binarization to obtain.
Figure BDA00011929920600000526
Is the number of pedestrians in the scene, and Z is a normalization factor for each pedestrian in the scene
Figure BDA00011929920600000527
The sum of the densities of (a) and (b) is 1. Structured population density map
Figure BDA00011929920600000528
Reflecting scene images
Figure BDA00011929920600000529
The density distribution and body shape information of all pedestrians.
And then, establishing a prediction model of the number of the crowd. The method specifically comprises the following steps:
firstly, establishing a deep convolution neural network, wherein the input of the neural network is a scene image
Figure BDA00011929920600000530
Output is corresponding to
Figure BDA00011929920600000531
Semantic structure diagram of crowd
Figure BDA00011929920600000532
Structured population density map
Figure BDA00011929920600000533
And
Figure BDA00011929920600000534
number of pedestrians
Figure BDA00011929920600000535
Thus, the structure of the neural network can be represented as a map
Figure BDA00011929920600000536
Second, sub-mapping
Figure BDA00011929920600000537
Using a soft maximum (Softmax) loss function, expressed as
Figure BDA00011929920600000538
Wherein
Figure BDA00011929920600000539
Is one of the outputs of the neural network,
Figure BDA00011929920600000540
to represent
Figure BDA00011929920600000541
The middle pixel position (h, w) and the value of channel i,
Figure BDA00011929920600000542
to represent
Figure BDA00011929920600000543
The value of the middle pixel position (h, w);
third step, sub-mapping
Figure BDA0001192992060000061
Using Euclidean loss function, expressed as
Figure BDA0001192992060000062
Wherein
Figure BDA0001192992060000063
Is a neural networkOne of the outputs is a high-frequency signal,
Figure BDA0001192992060000064
generated by the method described in equation (1).
Fourth, sub-mapping
Figure BDA0001192992060000065
Using Euclidean loss function, expressed as
Figure BDA0001192992060000066
Wherein
Figure BDA0001192992060000067
Is one of the outputs of the neural network,
Figure BDA0001192992060000068
is the number of people manually labeled.
The fifth step, the loss function of the whole neural network is
L＝Lc+λdLd+λbLbFormula (5)
The entire neural network is trained using a stochastic gradient descent and back propagation algorithm under a loss function L.
And finally, predicting the number of people in the scene image to be predicted by using the established model. The method specifically comprises the following steps: scene image to be predicted
Figure BDA0001192992060000069
Inputting the trained neural network, and outputting the population number
Figure BDA00011929920600000610
I.e. the result of the prediction of the number of the crowd.
In the above embodiment, the crowd quantity prediction method of the present invention first models the body appearance structure and the density distribution information of the pedestrian into two semantic scene models. On the basis, the original problem is converted into a multi-task learning problem, and a crowd quantity prediction model is established based on the deep neural network. And finally, predicting the accurate pedestrian number in the new scene image by using the trained crowd number prediction model.
Through the technical scheme, the embodiment of the invention develops the crowd quantity prediction algorithm applied to the video monitoring scene based on the deep learning technology. The invention can effectively model the body semantic structure information and the density distribution information of the pedestrian at the same time, thereby predicting the accurate crowd number.
The above description is only for the purpose of illustrating the preferred embodiments of the present invention and is not to be construed as limiting the invention, and any modifications, equivalents and improvements made within the spirit and principle of the present invention are intended to be included within the scope of the present invention.

Claims (2)

1. A crowd quantity prediction method based on human body apparent structure is characterized by comprising the following steps:
s1, obtaining a monitoring image data set for training a crowd quantity prediction model, including scene images
Figure FDA0002172096280000011
Artificially labeled head position P of pedestriantrainAnd scene depth map
Figure FDA0002172096280000012
And defining the algorithm targets as: predicting a scene image XtestNumber of pedestrians Ctest；
S2, modeling the apparent semantic structure of the pedestrian body in the monitoring image data set, and jointly modeling the density distribution and the body shape of the pedestrian, specifically comprising:
s21, collecting head positions P of all pedestrians according to the monitoring image datatrainAnd their respective scene depth values
Figure FDA0002172096280000013
Determining the position and size of each pedestrian image bounding box to derive a scene image
Figure FDA0002172096280000014
Middle cutting to obtain pedestrian image Itrain；
S22, displaying the pedestrian image ItrainRespectively inputting a single pedestrian semantic segmentation system for semantic segmentation;
s23, for each scene image
Figure FDA0002172096280000015
Restoring the segmentation results of all the pedestrians according to the original size and position to obtain a scene image
Figure FDA0002172096280000016
Semantic structure diagram of crowd
Figure FDA0002172096280000017
Figure FDA0002172096280000018
Reflecting scene images
Figure FDA0002172096280000019
Semantic structure information of body parts of all pedestrians;
s24, aiming at scene image
Figure FDA00021720962800000110
Performing combined modeling on the density distribution and the body shape of the pedestrians to obtain a structured crowd density map
Figure FDA00021720962800000111
Figure FDA00021720962800000112
Wherein p is
Figure FDA00021720962800000113
The position of the upper pixel in the image,
Figure FDA00021720962800000114
is a two-dimensional gaussian kernel to approximate the shape of a human head,
Figure FDA00021720962800000115
is a two-dimensional gaussian kernel to approximate the shape of the human body,
Figure FDA00021720962800000116
and
Figure FDA00021720962800000117
the central positions of the ith individual's head and body respectively,
Figure FDA00021720962800000118
is taken from Ptrain，
Figure FDA00021720962800000119
By
Figure FDA00021720962800000120
And head position PhDepth value of scene
Figure FDA00021720962800000121
Estimate to obtainhAnd σbAre respectively
Figure FDA00021720962800000122
And
Figure FDA00021720962800000123
respectively by the head position PhDepth value of scene
Figure FDA00021720962800000124
And body center position PbDepth value of scene
Figure FDA00021720962800000125
Estimated to obtain BmThe method comprises the following steps that A, a crowd semantic structure diagram B is obtained through binarization, C is the number of pedestrians in a scene image X, Z is a normalization coefficient so that the sum of the density of each pedestrian on D is 1, and a structured crowd density diagram D reflects the density distribution and body shape information of all pedestrians in the scene image X;
s3, establishing a prediction model of the crowd quantity according to the modeling result in the step S2, which specifically comprises the following steps:
s31, establishing a deep convolution neural network, wherein the input of the neural network is a scene image
Figure FDA0002172096280000021
Output is corresponding to
Figure FDA0002172096280000022
Prediction of the semantic structure of the crowd
Figure FDA0002172096280000023
Prediction of structured population density map
Figure FDA0002172096280000024
And prediction of pedestrian number in X
Figure FDA0002172096280000025
Thus, the structure of the neural network can be represented as a map
Figure FDA0002172096280000026
S32, child mapping
Figure FDA0002172096280000027
Using a soft maximum (Softmax) loss function, expressed as
Figure FDA0002172096280000028
Wherein
Figure FDA0002172096280000029
Is one of the outputs of the neural network,
Figure FDA00021720962800000210
to represent
Figure FDA00021720962800000211
The values of the (h, w) middle pixel position and the channel i, B is generated by the method described in step S23, and B (h, w) represents the value of the (h, w) middle pixel position in B;
s33, child mapping
Figure FDA00021720962800000212
Using Euclidean loss function, expressed as
Figure FDA00021720962800000213
Wherein
Figure FDA00021720962800000214
Is one of the outputs of the neural network, D is generated by the method of step S24;
s34, child mapping
Figure FDA00021720962800000215
Using Euclidean loss function, expressed as
Figure FDA00021720962800000216
S35 loss function of the whole neural network
L＝Lc+λdLd+λbLbFormula (5)
Training the whole neural network under a loss function L by using a random gradient descent and back propagation algorithm;
and S4, predicting the number of people in the scene image by using the prediction model.
2. The method for predicting the number of people based on the apparent structure of human body according to claim 1, wherein the step S4 of predicting the number of people in the scene image comprises: image of a scene to be predicted
Figure FDA00021720962800000217
Inputting the trained neural network and the output scene image
Figure FDA00021720962800000218
The pedestrian number C in (1) is the prediction result.
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