Subway public place pedestrian volume analysis method based on machine vision

Abstract

The invention discloses a subway public place people flow analysis method based on machine vision, and the method comprises the steps: S1, obtaining historical video flow data photographed by a subwaycamera, and extracting a subway people flow training data set; S2, loading a pre-training model of the YOLOv3 network, and initializing a network weight; S3, inputting the INRIA pedestrian data set and the subway pedestrian flow data training set into a YOLOv3 network for training to obtain a real target box label; S4, adding N times of up-sampling operation to obtain N feature maps with smallerscales, and changing the size of the input image; S5, performing network coarse training on the mixed data set, and optimizing the number, width and height of target boxes; S6, performing network finetraining on the subway people flow training data set; S7, detecting the subway people flow test data set by using the trained network model, and counting people flow; and evaluating the performance of the network model. The method is high in detection precision, the network is trained according to a strategy from coarse to fine. Boundary frame parameters are optimized, and the balance between thedetection speed and the precision is controlled.

Description

technical field [0001] The invention relates to the field of target detection, in particular to a method for analyzing the flow of people in a subway public place based on machine vision. Background technique [0002] The flow of people is an indispensable data for safety management and early warning of public places such as shopping malls and subway stations. Traditional video-based target detection is realized through the change of image information between frames. Compared with static target detection, it is easy to cause missed detection. Feature-based methods have poor generalization ability, and often extract low-level features, which cannot express high-level semantic features. In recent years, the research results of deep learning in the field of machine vision have been more and more applied to the field of target detection. It is further divided into region-based methods and regression-based methods. The regression-based method solves the problem of the balance ...

AI Technical Summary

Problems solved by technology

Traditional video-based target detection is realized through the change of image information between frames. Compared with static target detection, it is easy to cause missed detection.

Feature-based methods have poor generalization ability, and often extract low-level features, which cannot express high-level semantic features

Images