Corpus ID: 12787039

Anomaly Detection Based on Trajectory Analysis Using Kernel Density Estimation and Information Bottleneck Techniques

@inproceedings{Guo2015AnomalyDB,
  title={Anomaly Detection Based on Trajectory Analysis Using Kernel Density Estimation and Information Bottleneck Techniques},
  author={Yuejun Guo and Qing Xu and Yu Yang and Sheng Liang and Y. Liu and M. Sbert},
  year={2015}
}
In this paper, we propose a new technique to enhance the trajectory shape analysis by explicitly considering the speed attribute of trajectory data, as an effective and efficient way for anomaly detection. An object motion trajectory is mathematically represented by the Kernel Density Estimation, taking into account both the shape of the trajectory and the speed of the moving object. An unsupervised clustering algorithm, using the Information Bottleneck method, is employed for the trajectory… Expand
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