Quality-of-content (QoC)-driven rate allocation for video analysis in mobile surveillance networks

Nowadays, more and more videos are transmitted for video analytics purposes rather than human perceptions. In mobile surveillance networks, a cloud server collects videos delivered from multiple moving cameras and detects suspicious people in all the camera views. However, all the videos recorded by moving cameras such as phone or dash cameras are uploaded through bandwidth-limited wireless networks. Therefore, videos are required to be encoded with high compression ratio to satisfy the total data rate constraint, which may affect the video analyses (e.g., human detection/tracking and action recognition, etc.) performance due to the degraded video decoding qualities at the server side. In this paper, we propose an effective content-driven video source coding rate allocation scheme, which can improve the human detection success rate in mobile surveillance networks under a total data rate constraint. The proposed scheme allocates appropriate amount of data rate to each moving camera based on the corresponding content information (i.e., human detection results). A model of human detection accuracy based on object area and video quality is provided. The rate allocation problem is formulated as a convex optimization problem and can be solved by standard solvers. Simulations with real video sequences demonstrate the effectiveness of our proposed scheme.