FFS-VA: A Fast Filtering System for Large-scale Video Analytics

Surveillance video cameras are ubiquitous around us. Full-feature object-detection models such as YOLOv2 can automatically analyze surveillance videos in real-time with high accuracy while consuming huge computational resources. Directly applying these models for practical scenarios with large-scale deployed cameras requires prohibitively expensive computation. This, however, is both wasteful and unnecessary considering the fact that the concerned anomalous events occur rarely among these massive volumes of video streams. Therefore, in this paper, we propose a Fast Filtering System for Video Analytics (FFS-VA), a pipelined multi-stage video analyzing system, to make video analytics much cost-effective. FFS-VA is designed to filter out vast but non-target-object frames by two prepositive stream-specialized filters and a small full-function tiny-YOLO model, to drastically reduce the number of video frames arriving at the full-feature model in the back-end. FFS-VA presents a global feedback-queue mechanism to balance the processing rates of different filters in both intra-stream and inter-stream processes. FFS-VA also designs a dynamic batch technique to achieve an adjustable trade-off between throughput and latency. FFS-VA reasonably distributes all tasks on CPUs and GPUs to fully exploit the underlying hardware resources. We implement a FFS-VA prototype and evaluate FFS-VA against the state-of-the-art YOLOv2 under the same hardware and representative video workloads. The experimental results show that under a 10% target-object occurrence rate on two GPUs, FFS-VA can support up to 30 concurrent video streams (7x more than YOLOv2) in the online case, and obtain 3x speedup when offline analyzing a stream, with an accuracy loss of less than 2%.