Scrava: Super Resolution-Based Bandwidth-Efficient Cross-Camera Video Analytics

Abstract

Massively deployed cameras form a tightly connected network which generates video streams continuously. Benefiting from advances in computer vision, automated real-time analytics of video streams can be of practical value in various scenarios. As cameras become more dense, cross-camera video analytics has emerged. Combining video contents from multiple cameras for analytics is certainly more promising than single-camera analytics, which can realize cross-camera pedestrian tracking and cross-camera complex behavior recognition. Some works focused on optimization of cross-camera video analytic applications, but most of them ignore specific network situation between cameras and edge servers. Furthermore, most of them ignore the super resolution technique, which is proven to be a source of efficiency. In this paper, we first verify the potential gain of super resolution on cross-camera video analytic tasks. Then, we design and implement a cross-camera real-time video streaming analytic system, ${\mathsf {Scrava}}$ , which leverages super resolution to augment low-resolution videos and simultaneously reduce bandwidth consumption. ${\mathsf {Scrava}}$ enables real-time cross-camera video analytics and enhances video segments with the SR module under poor network conditions. We take cross-camera pedestrian tracking as an example, and experimentally verifies the effectiveness of super resolution on real-time cross-camera video analytics. Compared with using low-resolution video segments, ${\mathsf {Scrava}}$ can improve the F1 score by 47.16%, verifying the feasibility of exploiting super resolution to improve the performance of real-time cross-camera video analytic systems.