Spatio-temporal context analysis within video volumes for anomalous-event detection and localization

@article{Li2015SpatiotemporalCA,
  title={Spatio-temporal context analysis within video volumes for anomalous-event detection and localization},
  author={Nannan Li and Xinyu Wu and Dan Xu and Huiwen Guo and Wei Feng},
  journal={Neurocomputing},
  year={2015},
  volume={155},
  pages={309-319}
}
In this paper, we propose an anomaly-detection approach applied for video surveillance in crowded scenes. This approach is an unsupervised statistical learning framework based on analysis of spatiotemporal video-volume configuration within video cubes. It learns global activity patterns and local salient behavior patterns via clustering and sparse coding, respectively. Upon the composition-pattern dictionary learned from normal behavior, a sparse reconstruction cost criterion is designed to… CONTINUE READING
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