Vadivel Ayyasamy

EdgeRL-surveillance: adaptive decision making for resource-constrained video analytics using reinforcement learning

This paper uses the UCF-Crime dataset, which consists of actual surveillance video frames, to present an intelligent edge-cloud framework for Real-Time (RT) human activity identification, and proposes a Dynamic-DQN-based scheduler that dynamically executes computational tasks between the edge node and cloud node, powered by a Q/Q/K queuing model.