Research on Video Surveillance Face Recognition Algorithm Based on Improved Dense Network
In order to improve the ability of face recognition in video surveillance,it studies the use of motion history image algorithm to realize human tracking,and proposes an improved dense network.The results show that the tracking accuracy of the human tracking algorithm used in the study is as high as 99.5%,and the recognition accuracy of the proposed recognition algorithm can be above 99.7%,and can show high recognition accuracy for faces with different expression features.The above results show that the improved dense network can effectively improve the face recognition ability of video surveillance,which is of great significance to the intelligent development of urban surveillance.
video surveillancemotion history image algorithmimproved dense networkhuman trackingface recognition
国家科技期刊平台
NSTL
万方数据
维普
