Automatic anomaly activity detection is difficult in video surveillance applications due to
variations in size, type, shape, and objects' location. The traditional anomaly detection and …

With the development of information technology and the popularization of monitoring
network, how to quickly and automatically detect abnormal behaviors in surveillance video is …

The identification of abnormal behavior has several applications. There are several ways,
ranging from classical to deep learning based. It may be used to monitor campuses, banks …

Ensuring public safety in urban areas is a crucial element in maintaining a good quality of
life. The successful deployment of video surveillance systems depends heavily on the …

Due to exponential increase in crime rate, surveillance systems are being put up in malls,
stations, schools, airports etc. With the videos being captured 24x7 from these cameras, it is …

The pervasive use of cameras at indoor and outdoor premises on account of recording the
activities has resulted into deluge of long video data. Such surveillance videos are …

In the present technological world, surveillance cameras generate an immense amount of
video data from various sources, making its scrutiny tough for computer vision specialists. It …

Traditional video surveillance systems detect abnormal events via human involvement,
which is exhausting and erroneous, while computer vision-based automated anomaly …

In this paper, a deep learning-based anomaly detection (DLAD) system is proposed to
improve the recognition problem in video processing. Our system achieves complete …

In recent years, it is in public to use the surveillance cameras for continuous monitoring of
public and private spaces because of increasing crime. Most current surveillance systems …