Videos represent the primary source of information for surveillance applications. Video
material is often available in large quantities but in most cases it contains little or no …

Anomaly detection in videos refers to the identification of events that do not conform to
expected behavior. However, almost all existing methods tackle the problem by minimizing …

Motivated by the capability of sparse coding based anomaly detection, we propose a
Temporally-coherent Sparse Coding (TSC) where we enforce similar neighbouring frames …

We present an efficient method for detecting anomalies in videos. Recent applications of
convolutional neural networks have shown promises of convolutional layers for object …

This paper presents an anomaly detection method that is based on a sparse coding inspired
Deep Neural Networks (DNN). Specifically, in light of the success of sparse coding based …

Video Anomaly detection in videos refers to the identification of events that do not conform to
expected behavior. However, almost all existing methods cast this problem as the …

This paper presents a hierarchical framework for detecting local and global anomalies via
hierarchical feature representation and Gaussian process regression. While local anomaly …

With the massive deployment of distributed video surveillance systems, the automatic
detection of abnormal events in video streams has become an urgent need. An abnormal …

Automatically detecting anomalies in videos is a challenging problem due to non-
deterministic definitions of abnormal events and lack of sufficient training data. To address …

The LSTM Encoder-Decoder framework is used to learn representation of video sequences
and applied for detect abnormal event in complex environment. However, it generally fails to …