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 …

Video, as a key driver in the global explosion of digital information, can create tremendous
benefits for human society. Governments and enterprises are deploying innumerable …

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 …

Millions of cameras at edge are being deployed to power a variety of different deep learning
applications. However, the frames captured by these cameras are not always pristine-they …

Emerging deep learning-based video analytics tasks demand computation-intensive neural
networks and powerful computing resources on the cloud to achieve high accuracy. Due to …

Recent advances in computer vision and neural networks have made it possible for more
surveillance videos to be automatically searched and analyzed by algorithms rather than …

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 …

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 …

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

Edge computing has been getting a momentum with ever-increasing data at the edge of the
network. In particular, huge amounts of video data and their real-time processing …