The recent development of deep learning based fire detection techniques and the
availability of smart cameras able to execute these algorithms on the edge paved the way for …

Recently, there has been a significant increase in the use of deep learning and low-
computing edge devices for analysis of video-based systems, particularly in the field of …

Machine learning has demonstrated tremendous potential in solving real-world problems.
However, with the exponential growth of data amount and the increase of model complexity …

The combination of 5G/6G and edge computing has been envisioned as a promising
paradigm to empower pervasive and intensive computing for the Internet-of-Things (IoT) …

Video analytics plays a pivotal role in public safety (eg, criminal suspect detection, traffic flow
count, and illegal parking management), which assists the polices in monitoring all …

Real-time video analytics on edge devices has gained increasing attention across a wide
range of business areas. However, edge devices usually have limited computing resources …

Accurately measuring and understanding affective loads, such as cognitive and emotional
loads, is crucial in the field of human–robot interaction (HRI) research. Although established …

Our approach to action recognition is grounded in the intrinsic coexistence of and
complementary relationship between audio and visual information in videos. Going beyond …

Unmanned aerial vehicles (UAVs) offer advantages in serving as aerial small cells (ASCs) to
support public safety terrestrial cells (PSTCs) while providing pervasive coverage during …

There is an increasing demand for analyzing live surveillance video streams via large-scale
camera networks, particularly for applications in public safety and smart cities. To address …