A Fast and Tunable Privacy-Preserving Action Recognition Framework over Compressed Video

Qingfeng Zheng, Peijia Zheng, Weiqi Luo, Wei Lu

Abstract: Deep learning solutions for privacy anonymization in video action recognition face significant computational challenges and difficulties in collecting privacy labels. To address these issues, we propose a fast privacy-preserving action recognition framework based on compressed video bitstreams. Our framework introduces two novel anonymization methods for video bitstreams, transforming video data directly within the compressed domain into anonymized compressed data to protect sensitive video information. The anonymized and compressed video data exhibits a compact data representation, enhancing transmission efficiency. We propose a technique to reconstruct anonymized compressed data in the spatial domain and utilize deep learning technology for high-accuracy action recognition. The experiment results demonstrate that our framework excels in balancing privacy protection performance with action recognition, showing a significant trade-off advantage. Our approach also achieves an operational efficiency more than fivefold greater than existing methods.