  While large deep neural networks excel at general video analytics tasks, the
significant demand on computing capacity makes them infeasible for real-time
inference on resource-constrained end cam-eras. In this paper, we propose an
edge-assisted framework that continuously updates the lightweight model
deployed on the end cameras to achieve accurate predictions in adverse
environments. This framework consists of three modules, namely, a key frame
extractor, a trigger controller, and a retraining manager. The low-cost key
frame extractor obtains frames that can best represent the current environment.
Those frames are then transmitted and buffered as the retraining data for model
update at the edge server. Once the trigger controller detects a significant
accuracy drop in the selected frames, the retraining manager outputs the
optimal retraining configuration balancing the accuracy and time cost. We
prototype our system on two end devices of different computing capacities with
one edge server. The results demonstrate that our approach significantly
improves accuracy across all tested adverse environment scenarios (up to 24%)
and reduces more than 50% of the retraining time compared to existing
benchmarks.
