A Model of Saliency-Based Visual Attention for Rapid Scene Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
Detecting Irregularities in Images and in Video
International Journal of Computer Vision
Robust Real-Time Unusual Event Detection using Multiple Fixed-Location Monitors
IEEE Transactions on Pattern Analysis and Machine Intelligence
The evolution of video surveillance: an overview
Machine Vision and Applications
A dynamic hierarchical clustering method for trajectory-based unusual video event detection
IEEE Transactions on Image Processing
Non-parametric anomaly detection exploiting space-time features
Proceedings of the international conference on Multimedia
Adaptive rood pattern search for fast block-matching motion estimation
IEEE Transactions on Image Processing
| Hi-index | 0.00 |
In this paper, we proposed a fast and robust unsupervised framework for anomaly detection and localization in crowed scenes. Our method avoids modeling the normal state of the crowds which is a very complex task due to the large within class variance of the normal target appearance and motion patterns. For each video frame, we extract the spatial temporal features of 3D blocks and generate the saliency map using a block-based center-surround difference operator. Then, motion vector matrix is obtained by adaptive rood pattern search block-matching algorithm and distance normalization. Attractive motion disorder descriptor is proposed to measure the global intensity of anomalies in the scene. Finally, we classify the frames into normal and anomalous ones by a binary classifier. In the experiments, we compared our method against several state-of-the-art approaches on UCSD dataset which is a widely used anomaly detection and localization benchmark. As the only unsupervised approach, our method outputs competitive results with near real-time processing speed