Sparse coding-based anomaly detection has shown promising performance, of which the
keys are feature learning, sparse representation, and dictionary learning. In this paper, we …

Over the past decade, video anomaly detection has been explored with remarkable results.
However, research on methodologies suitable for online performance is still very limited. In …

This paper strives for the detection of real-world anomalies such as burglaries and assaults
in surveillance videos. Although anomalies are generally local, as they happen in a limited …

This paper brings forward one amongst the foremost significant applications of human
suspicious activity recognition that is termed as anomaly detection. A key concern of any …

Modern techniques for describing motion in videos are centred around velocity descriptors
based on optical flow. Realizing that acceleration is as important as velocity for describing …

Detecting and localizing anomalies in surveillance videos is an ongoing challenge. Most
existing methods are patch or trajectory-based, which lack semantic understanding of …

Global abnormal events form unique and distinct motion characteristics and category of
anomalies at image level rather than pixel level with less complexity compared to local …

In this paper, we propose an approach for anomaly detection in crowded scenes. For this
purpose, two important types of features that encode motion and appearance cues are …

Anonymous Detection is a device that identifies insignificant people's behavior. The most
prominent issue in laptop thinking and science is finding the wrong human behavior is very …

Video anomaly detection plays a prominent and challenging role for automated video
surveillance. To aim this, we propose a novel framework for local anomaly detection in …