Modeling abnormal spatiotemporal events is challenging since data belonging to abnormal
activities are less in the course of a surveillance stream. We solve this issue using a …

With the popularity of video surveillance technology, people are paying more and more
attention to how to detect abnormal states or events in videos in time. Therefore, real-time …

Anomaly detection is one of the most researched topics in computer vision and machine
learning. Manual detection of an oddity in a video costs significant time and money, so there …

Crowd anomaly detection is a key research area in vision-based surveillance. Most of the
crowd anomaly detection algorithms are either too slow, bulky, or power-hungry to be …

In this paper, a new method for detecting abnormal events in public surveillance systems is
proposed. In the first step of the proposed method, candidate regions are extracted, and the …

Visual perception is a transformative technology that can recognize patterns from
environments through visual inputs. Automatic surveillance of human activities has gained …

Recent advancements in computer vision led to the development of a real-time surveillance
system which ensures the safety and security of the people in public places. An aerial …

Real-time video anomaly detection and localization still prevail as a challenging task.
Autoencoders are expected to give high reconstruction error for abnormal events than …

Multimedia anomaly datasets play a crucial role in automated surveillance. They have a
wide range of applications expanding from outlier objects/situation detection to the detection …

Anomaly detection and location in crowded scenes have attracted a lot of attention in
computer vision research community recently due to the increased applications of intelligent …