Supervised learning algorithms have shown limited use in the field of anomaly detection due
to the unpredictability and difficulty in acquiring abnormal samples. In recent years …

Nowadays, Human Activity Recognition (HAR) is being widely used in a variety of domains,
and vision and sensor-based data enable cutting-edge technologies to detect, recognize …

Video Surveillance Systems (VSSs) are used in a wide range of applications including
public safety and perimeter security. They are deployed in places such as markets …

Crowd management is crucial for countries and organizations as it can lead to severe
consequences or serious safety concerns. Most of the existing research focus on addressing …

Individual abnormal behaviors vary depending on crowd sizes, contexts, and scenes.
Challenges such as partial occlusions, blurring, a large number of abnormal behaviors, and …

With the widespread use of closed-circuit television (CCTV) surveillance systems in public
areas, crowd anomaly detection has become an increasingly critical aspect of the intelligent …

Recognizing and localizing anomalous events in crowd scenes is a challenging problem
that has attracted the attention of researchers in computer vision. Surveillance cameras …

Forecasting crowd congestion is a critical aspect of crowd management, particularly in
dynamic and densely populated areas, such as urban centers, events, or pilgrimage sites. In …

Abnormal event detection is one of the most challenging tasks in computer vision. Many
existing deep anomaly detection models are based on reconstruction errors, where the …

Crowd gathering events deeply affect public safety. To enhance city management and avoid
potential risks, many algorithms are designed for crowd analysis and deployed on video …