  The real-time query of massive surveillance video data plays a fundamental
role in various smart urban applications such as public safety and intelligent
transportation. Traditional cloud-based approaches are not applicable because
of high transmission latency and prohibitive bandwidth cost, while edge devices
are often incapable of executing complex vision algorithms with low latency and
high accuracy due to restricted resources. Given the infeasibility of both
cloud-only and edge-only solutions, we present SurveilEdge, a collaborative
cloud-edge system for real-time queries of large-scale surveillance video
streams. Specifically, we design a convolutional neural network (CNN) training
scheme to reduce the training time with high accuracy, and an intelligent task
allocator to balance the load among different computing nodes and to achieve
the latency-accuracy tradeoff for real-time queries. We implement SurveilEdge
on a prototype with multiple edge devices and a public Cloud, and conduct
extensive experiments using realworld surveillance video datasets. Evaluation
results demonstrate that SurveilEdge manages to achieve up to 7x less bandwidth
cost and 5.4x faster query response time than the cloud-only solution; and can
improve query accuracy by up to 43.9% and achieve 15.8x speedup respectively,
in comparison with edge-only approaches.
