A method of crowd density analysis, detection and localization based on density map
A technology of crowd density and positioning method, applied in neural learning methods, instruments, biological neural network models, etc., can solve the problems of missed detection of small-sized heads and inability to provide crowd spatial information, so as to avoid missed detection problems and avoid local problems. Effects of information loss, complexity and redundancy avoidance
Active Publication Date: 2022-07-22
NANJING UNIV OF POSTS & TELECOMM
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This method combines the crowd density image with the target detection network, which solves the problem that the simple crowd counting cannot provide the spatial information of the crowd distribution, the specific location information of the characters, and the leakage caused by the small-sized head when the target detection network directly detects the crowd image. check question
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[0037]Below in conjunction with the accompanying drawings and specific embodiments, the present invention will be further clarified. It should be understood that these examples are only used to illustrate the present invention and are not used to limit the scope of the present invention. Modifications in the form of valence all fall within the scope defined by the appended claims of the present application.
[0038] A method of crowd density analysis, detection and localization based on density map, such as figure 1 , 2 As shown, aiming at crowd density detection, head detection and localization, and crowd counting, the crowd images in the data set used for training are firstly preprocessed, and the Gaussian filter is used to convert them into two-dimensional crowd density images. The depth separation hole convolutional network model learns the feature mapping function between the input image and the crowd density image, realizes an end-to-end prediction model, and integrates...
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The invention discloses a crowd density analysis, detection and positioning method based on a density map. The crowd images in the data set used for training are preprocessed, and a Gaussian filter is used to convert them into two-dimensional crowd density images, and then a depth image is designed. The separation hole convolutional network model learns the feature mapping function between the input image and the crowd density image, realizes an end-to-end prediction model, and integrates and sums the pixel values of the predicted density map to achieve crowd counting. Finally, the predicted crowd density map is input To RetinaNet target detection network to achieve head detection and localization. The invention realizes the classification of human heads and non-human heads in a highly crowded scene, and can solve the problems that crowd density analysis in a highly crowded scene cannot provide specific positioning and missed detection in crowd detection.
Description
technical field [0001] The invention relates to a crowd detection and positioning method based on density map classification, and mainly designs a depth separation hole convolutional neural network model to generate a high-quality crowd density map and input the crowd density map to a target detection network to better achieve target classification , to achieve crowd density analysis and human head detection and positioning, which belongs to the cross-application field of image processing, target detection and artificial intelligence. Background technique [0002] The purpose of crowd density analysis and head detection and positioning is to obtain the spatial distribution information of people and detect the specific positioning information of people through the crowd density map. It has become a hot issue in the field of computer vision. Wide range of applications. There are three main methods of crowd density analysis: detection-based methods, regression-based methods, a...
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IPC IPC(8): G06V20/52G06N3/04G06N3/08
CPCG06N3/08G06V20/53G06N3/045
Inventor 陈志陈璐岳文静
Owner NANJING UNIV OF POSTS & TELECOMM