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While large deep neural networks excel at general video analytics tasks, thesignificant demand on computing capacity makes them infeasible for real-timeinference on resource-constrained end cam-eras. In this paper, we propose anedge-assisted framework that continuously updates the lightweight modeldeployed on the end cameras to achieve accurate predictions in adverseenvironments. This framework consists of three modules, namely, a key frameextractor, a trigger controller, and a retraining manager. The low-cost keyframe extractor obtains frames that can best represent the current environment.Those frames are then transmitted and buffered as the retraining data for modelupdate at the edge server. Once the trigger controller detects a significantaccuracy drop in the selected frames, the retraining manager outputs theoptimal retraining configuration balancing the accuracy and time cost. Weprototype our system on two end devices of different computing capacities withone edge server. The results demonstrate that our approach significantlyimproves accuracy across all tested adverse environment scenarios (up to 24%)and reduces more than 50% of the retraining time compared to existingbenchmarks.

Edge-Assisted On-Device Model Update for Video Analytics in Adverse Environments