Analysis of People Flow Image Detection System Based on Computer Vision Sensor

Abstract

In order to meet the needs of accurately grasping the situation of people in the mall at all times, the author proposes an analysis method based on computer vision for people flow image detection system. This method combines the HOG feature with the SVM classifier, detects pedestrians through dual cameras, and builds an experimental research platform for dual-camera joint detection of pedestrians. The result shows that the error rate of human flow detected by the author’s method is the lowest of 0% and the highest of 6.25%. Conclusion. This method has a good effect on the statistics of the number of people in the shopping mall and can reduce the workload of the monitoring personnel in the shopping mall.