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 …

Recent years have witnessed the dramatic growth of Internet video traffic, where the video
bitstreams are often compressed and delivered in low quality to fit the streamer's uplink …

The explosive growth of video traffic on today's Internet promotes the rise of Neural-
enhanced Video Streaming (NeVS), which effectively improves the rate-distortion trade-off …

Standardized lossy video coding is at the core of almost all real-world video processing
pipelines. Rate control is used to enable standard codecs to adapt to different network …

Cameras are widely used in many fields, eg, intelligent transportation, autonomous driving,
surveillance, etc. It is thus vital to conduct video analytics in an efficient and accurate …

Real-time edge-cloud video analytics systems have been widely used to support such
applications as traffic counting, surveillance, autonomous driving, Metaverse, etc. In such a …

Deep learning inference on streaming media data, such as object detection in video or
LiDAR feeds and text extraction from audio waves, is now ubiquitous. To achieve high …

Camera orientations (ie, rotation and zoom) govern the content that a camera captures in a
given scene, which in turn heavily influences the accuracy of live video analytics pipelines …

With the widespread deployment of edge computing, the focus has shifted to machine-
centric live video streaming, where endpoint-collected videos are transmitted over networks …

The delay to obtain analysis results is an important metric in video analytics. Previous work
has focused on reducing frame transmission and inference delay to optimize total delay …