Automatic video surveillance is an active research area in recent times to enhance security
features. Based on the crowd behavior, the normal and abnormal scenarios can be detected …

For safeguarding and monitoring purposes, public places are equipped with surveillance
cameras. Timely and accurate identification of suspicious activities is paramount to securing …

Automatic identification of anomalies in video surveillance is an interesting research field.
Even though interactive multimedia anomaly detection algorithms have been developed, it is …

Detecting anomalies in videos is not only crucial but also an intriguing task in surveillance
systems. It is a sequential modeling problem in nature that requires careful selection of …

Anomaly detection is finding various anomalous activities taking place in the video. Using an
unsupervised learning technique, surveillance videos identify various real-time video …

This paper presents a novel deep learning-based approach for anomaly detection in
surveillance films. A deep network that has been trained to recognize objects and human …

Anomaly detection in real time surveillance videos is a challenging task due to the scenario
dependency, duration and multiple occurrences of anomalous events. Typically, weakly …

This paper describes a method for learning anomaly behavior in the video by finding an
attention region from spatiotemporal information, in contrast to the full-frame learning. In our …

Anomaly detection from crowd is a widely addressed problem in the field of computer vision.
It is an essential part of video surveillance and security. In surveillance videos, very little …

Video surveillance for real-world anomaly detection and prevention using deep learning is
an important and difficult research area. It is imperative to detect and prevent anomalies to …