Recent Machine Learning (ML) techniques enable new features on video analytic
applications. Still, video pro-cessing requires intensive computing power and …

With the rise of artificial intelligence, various video analytics models have been applied in
many fields. Numerous studies are preoccupied with expanding the size of the model to …

Video analysis typically requires a significant amount of computing resources and energy.
Traditional cloud-based video analysis relies on concentrating computing resources in the …

With increasingly deployed cameras and the rapid advances of Computer Vision, large-
scale live video analytics becomes feasible. However, analyzing videos is compute …

To enable computation-intensive video analytics, streaming video data and offloading
computation from the source to the inference server running deep neural networks has now …

Deep learning algorithms are an essential component of video analytics systems, in which
the content of a video stream is analyzed. Although numerous studies target optimizing …

Geographical information has shown great potential in optimizing the resource allocation for
Video-on-Demand (VoD) systems, eg, the VoD service provider can allocate more …

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 …

Edge computing has opened new doors for real-time video analytics applications due to its
ability to offer significantly faster response times by processing videos near the source …

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 …