Video surveillance systems are integral to bolstering safety and security across multiple
settings. With the advent of deep learning (DL), a specialization within machine learning …

Fast and automated recognizing of abnormal behaviors in crowded scenes is significantly
effective in increasing public security. The traditional procedure of recognizing abnormalities …

Intelligent multimedia surveillance concerns the analysis of multiple sensing inputs including
video and audio streams, radio-frequency identification (RFID) and depth data. These data …

With the advancement of vision-based artificial intelligence, the proliferation of the Internet of
Things connected cameras, and the increasing societal need for rapid and equitable …

In 2007, only 13 percent of US law enforcement agencies reported having one or more
surveillance cameras permanently mounted in a public area, partly because surveillance …

Video surveillance devices are a valuable tool in various contexts to automate different
danger conditions and enable security guards to make effective decisions to improve asset …

In the face of burgeoning population growth, ensuring security during public events, familial
gatherings, and in high-traffic areas has become increasingly challenging. The manual …

Dependable utilization of computer vision applications, such as smart surveillance, requires
training deep learning networks on datasets that sufficiently represent the classes of interest …

Realizing the automated and online detection of crowd anomalies from surveillance CCTVs
is a research-intensive and application-demanding task. This research proposes a novel …

Crowd density level evaluates the number of people gathering in a particular area or scene,
typically based on videos generated from real-time closed-circuit television (CCTV) …