Towards Performance Clarity of Edge Video Analytics

Abstract

Edge video analytics is becoming the solution to many safety and management tasks. Its wide deployment, however, must first address the tension between inference accuracy and resource (compute/network) cost. This has led to the development of video analytics pipelines (VAPs), which reduce resource cost by combining deep neural network compression and speedup techniques with video processing heuristics. Our measurement study, however, shows that today's methods for evaluating VAPs are incomplete, often producing premature conclusions or ambiguous results. This is because each VAP's performance varies largely across videos and time, and is sensitive to different subsets of video content characteristics. We argue that accurate VAP evaluation must first characterize the complex interaction between VAPs and video characteristics, which we refer to as VAP performance clarity. Following this concept, we design and implement Yoda, the first VAP benchmark to achieve performance clarity. Using primitive-based profiling and a carefully curated bench-mark video set, Yoda builds a performance clarity profile for each VAP to precisely define its accuracy vs. cost trade-off and its relationship with video characteristics. We show that Yoda substantially improves VAP evaluations by (1) providing a comprehensive, transparent assessment of VAP performance and its dependencies on video characteristics; (2) explicitly identifying fine-grained VAP behaviors that were previously hidden by large performance variance; and (3) revealing strengths/weaknesses among different VAPs and new design opportunities.