Video analytics with deep learning techniques has generated immense interest in academia
and industry, captivating minds with its transformative potential. Deep learning techniques …

As mobile devices continuously generate streams of images and videos, a new class of
mobile deep vision applications are rapidly emerging, which usually involve running deep …

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 …

Continuous learning has recently shown promising results for video analytics by adapting a
lightweight" expert" DNN model for each specific video scene to cope with the data drift in …

Edge-cloud collaborative video analytics is transforming the way data is being handled,
processed, and transmitted from the ever-growing number of surveillance cameras around …

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 …

The digital age has brought a significant increase in video traffic. This traffic growth, driven
by rapid internet advancements and a surge in multimedia applications, presents both …

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 …

The advent of high-accuracy and resource-intensive deep neural networks (DNNs) has
fulled the development of live video analytics, where camera videos need to be streamed …

Smart wearable devices have become more and more popular in our daily life due to their
unique power of “wearing-while-using.” However, requirements of light weight and compact …