Image Segmentation Based Privacy-Preserving Human Action Recognition For Anomaly Detection

Human Action Recognition and Anomaly Detection significantly improved automatic video analysis, assisted living, and video-based surveillance. The focus of this work is on those applications where privacy protection is required, such as surveillance and assisted living. RGB video data is the most common source for human action recognition. However, RGB data also contains privacy-related data, such as the identity of the target. In this paper, we prove that human action recognition accuracy mostly depends on contextual data, rather than on privacy-related data. Therefore, human target data can be occluded by using an image segmentation mask. The proposed method achieves almost similar accuracy in comparison with the privacy case and provides the platform for privacy-preserving anomaly detection. Simulations are performed on the two popular datasets for human action recognition, i.e. UCF101 and HMDB51.