A Method of Video Crowd Counting Based on LSTM-Weighted Neural Network

Abstract

The invention discloses a method for counting crowds in a video based on a weighted convolutional neural network based on long-term and short-term memory. First, perspective images are estimated according to different scenes, and then an adaptive density map of crowds is generated; After sampling, enter the neural network, and train the network based on the image and its corresponding real adaptive density map; use the trained network to estimate the density map of the input image, and predict the number of people based on the density map. Aiming at the scale difference of the crowd in the scene, the present invention uses a sub-scale loss function to measure the different scale features learned by the network. To solve the problem of uneven distribution of crowds, the present invention weighs the contributions of different regions through a weighted loss function. At the same time, the present invention acquires the associated information between adjacent frames through long-term and short-term memory, and performs post-processing on the predicted number of people through smoothing filtering, thereby improving the accuracy of crowd counting.

Description

technical field [0001] The invention belongs to the field of intelligent monitoring, in particular to a method for counting crowds on video images by using a long-short-term memory-weighted neural network. Background technique [0002] With the rapid increase of the global population, behaviors such as crowding and stampede in group events continue to increase. On New Year's Day in 2015, 35 people were killed in a vicious stampede on the Bund in Shanghai. Religious activities around the world are also prone to large-scale stampedes, which have extremely serious social impacts. Therefore, monitoring the number of people in real time through surveillance video in public places, and sending an alarm when the number exceeds a certain threshold can effectively avoid vicious group incidents, and can also provide a basis for crowd control. In addition, video-based crowd counting can also be extended to other fields, such as counting the traffic flow on the road and then analyzing ...

AI Technical Summary

Problems solved by technology

However, crowd counting using deep learning still has challenges such as uneven crowd distribution and different scales

At the same time, the current mainstream deep learning crowd counting methods usually only predict the number of people in still images, but cannot predict the number of people in video images with rich temporal information.

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