A Low-Cost Video Analytics System with Velocity Based Configuration Adaptation in Edge Computing

In this paper, we propose a low-cost video analytics system analyzing multiple video streams efficiently under limited resource. The objective of our proposed system is to find the best configuration decision of frame sampling rate for multiple video streams in order to minimize the accuracy degradation in the shared limited resource, utilizing the velocity features extracted from video context in low cost. To evaluate the proposed algorithm, we use a subset of videos from VIRAT dataset. The results show that our video analytics system outperforms the existing video analytics systems on resource-accuracy trade-offs and reduces the high profiling cost of them.