Advances in deep learning have shown promising potential in scalable video analytics in
the cloud. However, in constrained settings, it is not feasible to send every video frame from …

We propose a novel spatio-temporal filtering technique to improve the per-pixel prediction
map, by leveraging the spatio-temporal smoothness of the video signal. Different from …

Recent advances in video processing utilizing deep learning primitives achieved
breakthroughs in fundamental problems in video analysis such as frame classification and …

Recent advances in video processing utilizing deep learning primitives achieved
breakthroughs in fundamental problems in video analysis such as frame classification and …

Many edge applications rely on expensive Deep-Neural-Network (DNN) inference-based
video analytics. Typically, a single instance of an inference service analyzes multiple …

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 …

Recent advances in neural networks (NNs) have enabled automatic querying of large
volumes of video data with high accuracy. While these deep NNs can produce accurate …

Advances in deep learning have enabled brand new video analytics systems and
applications. Existing systems research on real-time video event detection does not consider …

To cope with the high resource (network and compute) demands of real-time video analytics
pipelines, recent systems have relied on frame filtering. However, filtering has typically been …

Deep neural nets enabled sophisticated information extraction out of images, including
video frames. Recently, there has been interest in techniques and algorithms to enable …