RESPIRE: Reducing Spatial–Temporal Redundancy for Efficient Edge-Based Industrial Video Analytics

Video camera plays a growing important role in advancing industrial control towards a higher level of automation. Thus, video analytics become highly demanded, especially for low-latency and high-accuracy analytic results. Yet, the data volume produced by camera clusters is prohibitively high. In this article, we propose Respire, a system that can remove redundant frames for reducing the cost of transmission and processing based on edge computing nodes, while maintaining useful frames for high analytic accuracy. Specifically, Respire incorporates a new way for characterizing the spatial–temporal redundancy between frames. Then, Respire prioritizes the uploading of frames for redundancy reduction. As the search space of the entire collected frames is exponential for the set of frames containing the maximal information, we jointly consider offline and online pruning of frames and propose a heuristic algorithm to reduce the search space. Extensive real-world dataset-based experiments demonstrate that the proposed system can significantly reduce communication and computation costs, while providing sufficient information for guaranteed video analytic accuracy.