Real-time distributed video analytics for privacy-aware person search

In this work, a novel distributed privacy-aware person search (PAPS) model has been proposed which circumvents the privacy risks. An intelligent IoT surveillance system has been designed to integrate the PAPS model for real-time distributed privacy-aware person search from surveillance videos. An important aspect of the intelligent surveillance system, particularly person search, is the visual feedback at the output, with ranked results of person images at the user-end. Therefore, even if edge processing is performed, there is still a need to store and transmit the cropped person images to the cloud server for displaying the results at the user-end. However, storing or transmission of videos/images to cloud-servers leads to privacy issues. The proposed PAPS model eliminates the need to store or transmit the images/videos while performing person search, thereby addressing the privacy concerns. The proposed system is easily scalable to incorporate more camera nodes to enhance the surveillance coverage as majority of the processing is performed at the edge servers, with a small amount of fog-processing. A very minimal amount of cloud-processing is performed only when a query is raised at the user-end. Only the processed and encoded data is transmitted across the edge, fog and the cloud servers, which protects privacy and significantly reduces bandwidth costs. Further, a new evaluation criterion, Person Capacity, has been proposed to evaluate the feasibility of an edge-based system to be deployed at crowded locations. The performance evaluation of our system, on our own video dataset, as well as the PRW, and CUHK-SYSU dataset for person search demonstrates that the proposed system achieves state-of-the-art or competitive performance while performing in real-time for practical scenarios.