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 …

Real-time analytics on video data demands intensive computation resources and high
energy consumption. Traditional cloud-based video analytics relies on large centralized …

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 …

Video cameras have been deployed at scale today. Driven by the breakthrough in deep
learning (DL), organizations that have deployed these cameras start to use DL-based …

Live video streaming services have experienced significant growth since the emergence of
social networking paradigms in recent years. In this scenario, adaptive bitrate streaming …

The proliferation of camera-enabled devices and large video repositories has led to a
diverse set of video analytics applications. These applications rely on video pipelines …

Trustworthy and real-time video surveillance aims to analyze the live camera streams in a
privacy-preserving manner for the decision-making of various advanced services, such as …

Edge intelligence (EI) is becoming one of the research hotspots among researchers, which
is believed to help empower intelligent transportation systems (ITS). ITS generates a large …

The Internet of Things (IoT) is on the verge of a major paradigm shift. In the IoT system of the
future, IoFT, the “cloud” will be substituted by the “crowd” where model training is brought to …