Video analytics pipelines incorporate on-premise edge servers to lower analysis latency,
ensure privacy, and reduce bandwidth requirements. However, compared to the cloud, edge …

Video analytics pipelines have steadily shifted to edge deployments to reduce bandwidth
overheads and privacy violations, but in doing so, face an ever-growing resource tension …

Numerous edge applications that rely on video analytics demand precise, low-latency
processing of multiple video streams from cameras. When these cameras are mobile, such …

Video analytics applications use edge compute servers for processing videos. Compressed
models that are deployed on the edge servers for inference suffer from data drift where the …

Mobile video analytics applications, such as smart driving, VR/AR, and video surveillance,
have become increasingly popular due to the proliferation of mobile devices. These …

Emerging Internet of Things (IoT) and mobile computing applications are expected to
support latency-sensitive deep neural network (DNN) workloads. To realize this vision, the …

Powered by deep learning, video analytic applications process millions of camera feeds in
real‐time to extract meaningful information from their surroundings. And this number grows …

The advent of edge computing has made real-time intelligent video analytics feasible.
Previous works, based on traditional model architecture (eg, CNN, RNN, etc.), employ …

Edge video analytics is becoming the solution to many safety and management tasks. Its
wide deployment, however, must first address the tension between inference accuracy and …

The resource efficiency of video analytics workloads is critical for large-scale deployments
on edge nodes and cloud clusters. Recent advanced systems have benefited from …