The Research of Gait Recognition Based on Deep Learning: A Case Study of the Missing Elderly

Abstract

Abstract In our daily life, we often see some notices for the missing elderly. As we all know, the elderly people with Alzheimer’s disease or memory impairment are more likely and more often to get lost. Therefore, I hope that I can make some contribution to help solve this problem. For this reason, the author a detection and tracking system with interactive interface was designed by the author based on gait recognition for the elder people who are under the risk of getting lost. The walking characteristics of the elderly can be extracted from the gait image data of the elderly provided by their families. Combined with the video streaming information documented by the cameras of the public security organ, the detection and tracking system can identify the missing elderly at a long distance, in this way, it provides clues for searching the missing elderly. In this paper, all experiments were under the Windows 7 operating system. Python was used to invocate yolov3 model for character detection. The foreground images were obtained by applying background subtraction in the static background, and then gray processing, Gauss blurring, binarization and other image operations were done to extract the image features and obtain the GEI gait energy map with the help of gait feature extraction technology. The image data were stored in the database, and then pedestrian identity detection was carried out through an input video or by real-time cameras. In order to test conveniently, an interactive interface based on PyQt4 is designed, which can realize functions such as register pedestrian names on site, acquire pedestrian gait energy map and establish database, refresh background, detect and recognize pedestrians. Moreover, it supports arbitrary switching between real-time camera detection and input video detection.