Pedestrian flow monitoring method and device, storage medium and equipment
A monitoring device and a technology for people flow, applied in the information field, can solve the problem of inaccurate counting of people flow, and achieve the effect of good robustness, accurate and rapid statistics
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Embodiment 1
[0038] see figure 1 , showing a flow chart of a method for monitoring human flow provided by the present invention, detailed as follows:
[0039] Step S101, acquiring the target image of the pedestrian to be monitored in the video image;
[0040] Wherein, the source of the acquired video images may be cameras installed in various places, such as image information of corresponding areas collected by cameras installed in shopping malls, stations and other public places, or some video images.
[0041] Step S102, using a deep residual network to extract the features of the target image;
[0042] Wherein, based on each residual block sequentially connected in the deep residual network, the feature extraction is performed on the target image, and the head and shoulders of a single person, the box of the crowd area, the confidence level and the density map information of the target image are obtained according to the network structure; Any residual block includes an identity map an...
Embodiment 2
[0054] see figure 2 , which is a flow chart of training a crowd detection model for a crowd monitoring method provided by the present invention, and is described in detail as follows:
[0055] Step S201, mark the crowd area of multiple sample images, mark the head and shoulders of the person when the sample image is a single person image, and mark the group frame when the sample image is a crowd image, and according to each of the sample images Build a crowd detection model in the marked area;
[0056] Step S202, train the crowd detection model through a plurality of training samples, and generate the crowd detection model capable of classifying and locating regions in the target image according to crowd characteristics.
[0057] Specifically, when training the model, it is necessary to prepare the data labels to be detected, that is, sample data (including sample images of various densities), for example, labeling the input sample images according to the clear human head ...
Embodiment 3
[0068] see image 3 , which is a structural block diagram of a human flow monitoring device provided by the present invention, and is described in detail as follows:
[0069] Image acquisition module 31, for acquiring the target image of the pedestrian to be monitored in the video image;
[0070] The feature extraction module 32 utilizes the depth residual network to extract the features in the target image;
[0071] Wherein, based on each residual block sequentially connected in the deep residual network, the feature extraction of the target image is performed, any residual block includes an identity map and at least two convolutional layers, and any residual The identity map of the block is directed from the input end of any residual block to the output end of any residual block.
[0072] The crowd detection module 33 uses the crowd detection model to classify and locate the unmanned area, single person area and crowd area in the target image;
[0073] Wherein, labeling t...
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