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 …

Driven by plummeting camera prices and advances of video inference algorithms, video
cameras are deployed ubiquitously and organizations usually rely on live video analytics to …

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 …

Deep learning (DL) has shown promising results on complex computer vision tasks for video
stream analytics recently. However, DL-based analytics typically requires intensive …

Video cameras have been deployed at scale today. Driven by the breakthrough in deep
learning (DL), organizations that have deployed these cameras start to use DL-based …

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 …

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 …

Deep learning with Deep Neural Networks (DNNs) can achieve much higher accuracy on
many computer vision tasks than classic machine learning algorithms. Because of the high …

Mainstream is a new video analysis system that jointly adapts concurrent applications
sharing fixed edge resources to maximize aggregate result quality. Mainstream exploits …

With today's ubiquitous deployment of video cameras and other edge devices, progress in
edge computing is happening at an incredible speed. Yet, one aspect of real-time video …