Object Detection Oriented Privacy-Preserving Frame-Level Video Anomaly Detection

With the rapid development of intelligent surveillance, video anomaly detection has become a popular topic in related areas of artificial intelligence. In this work, the main focus is on those applications where the privacy of human targets is concerned, such as outdoor and indoor surveillance and smart living systems. Video frames are the most common recorded and processed information source for human anomaly detection. However, video frames also contain privacy-sensitive information such as facial information and identification of human targets. This paper provides a privacy-preserving anomaly detection framework that introduces image segmentation masks to protect the privacy of the human targets. Meanwhile, object detection is implemented to improve anomaly detection performance by incorporating contextual information. The proposed method uses the ST-AE and CONV-AE models, which were trained and tested on the popular anomaly detection datasets UCSD Ped1 and Ped2. Experiments confirm that when image segmentation masks are applied to preserve human targets' privacy information, the anomaly detection models still achieve good performances with the orientation of object detection.