A Quality-of-Content-Based Joint Source and Channel Coding for Human Detections in a Mobile Surveillance Cloud

Abstract

More than 70% of consumer mobile Internet traffic will be mobile video transmissions by 2019. The development of wireless video transmission technologies has been boosted by the rapidly increasing demand of video streaming applications. Although more and more videos are delivered for video analysis (e.g., object detection/tracking and action recognition), most existing wireless video transmission schemes are developed to optimize human perception quality and are suboptimal for video analysis. In mobile surveillance networks, a cloud server collects videos from multiple moving cameras and detects suspicious persons in all camera views. Camera mobility in smartphones or dash cameras implies that video is to be uploaded through bandwidth-limited and error-prone wireless networks, which may cause quality degradation of the decoded videos and jeopardize the performance of video analyses. In this paper, we propose an effective rate-allocation scheme for multiple moving cameras in order to improve human detection (content) performance. Therefore, the optimization criterion of the proposed rate-allocation scheme is driven by quality of content (QoC). Both video source coding and application layer forward error correction coding rates are jointly optimized. Moreover, the proposed rate-allocation problem is formulated as a convex optimization problem and can be efficiently solved by standard solvers. Many simulations using High Efficiency Video Coding standard compression of video sequences and the deformable part model object detector are carried, and results demonstrate the effectiveness and favorable performance of our proposed QoC-driven scheme under different pedestrian densities and wireless conditions.