With the proposal of edge computing, lots of intelligence applications have made significant
progress. For enormous video analysis, how to further accelerate the process is still a major …

Edge computing is being widely used for video analytics. To alleviate the inherent tension
between accuracy and cost, various video analytics pipelines have been proposed to …

High-definition (HD) cameras for surveillance and road traffic have experienced tremendous
growth, demanding intensive computation resources for real-time analytics. Recently …

Abstract This paper introduces Multi-Resolution Rescored Byte-Track (MR2-ByteTrack) a
novel video object detection framework for ultra-low-power embedded processors. This …

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 …

Detection-driven real-time video analytics require continuous detection of objects contained
in the video frames using deep learning models like YOLOV3, EfficientDet, etc. However …

Video object detection, a basic task in the computer vision field, is rapidly evolving and
widely used. In recent years, deep learning methods have rapidly become widespread in the …

Optical flow, which expresses pixel displacement, is widely used in many computer vision
tasks to provide pixel-level motion information. However, with the remarkable progress of the …

Object detection in videos is an important task in computer vision for various applications
such as object tracking, video summarization and video search. Although great progress has …

Abundant redundancies exist in video streams, thereby pointing to opportunities to save
computations. Towards this end, we propose the Adaptive Network across Time (ANT) …