Enhancing Video Surveillance and Behavior Recognition With Deep Learning While Ensuring Privacy Protection

With the rapid advancement of technology, deep learning has demonstrated significant application potential in the fields of video surveillance and behavior recognition. This paper explores the application of an improved Convolutional Neural Network (CNN)-based deep learning model in video behavior recognition and discusses privacy protection strategies. By incorporating multi-scale feature fusion mechanisms, spatiotemporal attention mechanisms, and Long Short-Term Memory (LSTM) networks, the designed deep learning model achieved notable performance improvements on the UCF-101 dataset, with an accuracy rate of 95.8%, precision of 96.5%, recall of 95.2%, and an F1 score of 95.8%. Additionally, this paper outlines a comprehensive privacy protection strategy that includes data anonymization, encrypted transmission, and access control, effectively safeguarding personal privacy. Experimental results indicate that although the privacy protection measures led to an increase in data processing time by approximately 40% and a decrease in model performance by about 2.5%, they significantly enhanced data security. This study provides valuable insights and references for the application of deep learning in video surveillance and behavior recognition, as well as its associated privacy protection.