Crucio: End-to-End Coordinated Spatio-Temporal Redundancy Elimination for Fast Video Analytics

Andong Zhu, Sheng Zhang, Xiaohang Shi, Ke Cheng, Hesheng Sun, Sanglu Lu

Abstract: Video Analytics Pipeline (VAP) usually relies on traditional codecs to stream video content from clients to servers. However, such analytics-agnostic codecs preserve considerable pixels not relevant to achieving high analytics accuracy, incurring a large end-to-end delay. Despite the significant efforts of pioneers, they fall short as they resisted complete redundancy elimination. Achieving such a goal is extremely challenging, and naive design without coordination can result in the benefits of redundancy elimination being counterbalanced by intolerable delays introduced. We present CRUCIO, an end-to-end coordinated spatio-temporal redundancy elimination system for edge video analytics. CRUCIO leverages reshaped asymmetric autoencoders for end-to-end frame filtering (temporally) and coordinated intra-frame (spatially), inter-frame (temporally) compression. Furthermore, CRUCIO can decode the compressed key frames all in one go and support adaptive VAP batch size for delay optimization. Extensive evaluations reveal significant end-to-end delay reductions (at least 31% under an accuracy target of 0.9) in CRUCIO compared to the state-of-the-art VAP redundancy elimination methods (e.g., DDS, Reducto, STAC, etc).