  This paper proposes a novel edge computing enabled real-time video analysis
system for intelligent visual devices. The proposed system consists of a
tracking-assisted object detection module (TAODM) and a region of interesting
module (ROIM). TAODM adaptively determines the offloading decision to process
each video frame locally with a tracking algorithm or to offload it to the edge
server inferred by an object detection model. ROIM determines each offloading
frame's resolution and detection model configuration to ensure that the
analysis results can return in time. TAODM and ROIM interact jointly to filter
the repetitive spatial-temporal semantic information to maximize the processing
rate while ensuring high video analysis accuracy. Unlike most existing works,
this paper investigates the real-time video analysis systems where the
intelligent visual device connects to the edge server through a wireless
network with fluctuating network conditions. We decompose the real-time video
analysis problem into the offloading decision and configurations selection
sub-problems. To solve these two sub-problems, we introduce a double deep Q
network (DDQN) based offloading approach and a contextual multi-armed bandit
(CMAB) based adaptive configurations selection approach, respectively. A
DDQN-CMAB reinforcement learning (DCRL) training framework is further developed
to integrate these two approaches to improve the overall video analyzing
performance. Extensive simulations are conducted to evaluate the performance of
the proposed solution, and demonstrate its superiority over counterparts.
