Unsupervised Abnormal Behaviour Detection with Overhead Crowd Video

Unsupervised Abnormal Behaviour Detection with Overhead Crowd Video

Abstract

Due to the increasing threat of terrorism, it has become more and more important to detect abnormal behaviour in public areas. In this paper, we introduce a system to identify pedestrians with abnormal movement trajectories in a scene using a data-driven approach. Our system includes two parts. The first part is an interactive tool that takes an overhead video as an input and tracks the pedestrians in a semi-automatic manner. The second part is a data-driven abnormal trajectories detection algorithm, which applies iterative k-means clustering to find out possible paths in the scene and thereby identifies those that do not fit well in any paths. Since the system requires only RGB video, it is compatible with most of the closed-circuit television (CCTV) systems used for security monitoring. Furthermore, the training of the abnormal trajectories detection algorithm is unsupervised and fully automatic. It means that the system can be deployed into a new location without manual parameter tuning and training data annotations. The system can be applied in indoor and outdoor environments and is best for automatic security monitoring.

Publication

Shoujiang Xu, Edmond S. L. Ho, Nauman Aslam and Hubert P. H. Shum,
"Unsupervised Abnormal Behaviour Detection with Overhead Crowd Video",
Proceedings of the 2017 International Conference on Software Knowledge Information Management and Applications (SKIMA)

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References

BibTeX

@inproceedings{xu17unsupervised,
 author={Xu, Shoujiang and Ho, Edmond S. L. and Aslam, Nauman and Shum, Hubert P. H.},
 booktitle={Proceedings of the 2017 International Conference on Software Knowledge Information Management and Applications},
 series={SKIMA '17},
 title={Unsupervised Abnormal Behaviour Detection with Overhead Crowd Video},
 year={2017},
 month={Dec},
 pages={1--6},
 numpages={6},
 doi={10.1109/SKIMA.2017.8294092},
 issn={2573-3214},
 location={Colombo, Sri Lanka},
}

EndNote/RefMan

TY  - CONF
AU  - Xu, Shoujiang
AU  - Ho, Edmond S. L.
AU  - Aslam, Nauman
AU  - Shum, Hubert P. H.
T2  - Proceedings of the 2017 International Conference on Software Knowledge Information Management and Applications
TI  - Unsupervised Abnormal Behaviour Detection with Overhead Crowd Video
PY  - 2017
Y1  - Dec 2017
SP  - 1
EP  - 6
DO  - 10.1109/SKIMA.2017.8294092
SN  - 2573-3214
ER  - 

Plain Text

Shoujiang Xu, Edmond S. L. Ho, Nauman Aslam and Hubert P. H. Shum, "Unsupervised Abnormal Behaviour Detection with Overhead Crowd Video," in SKIMA '17: Proceedings of the 2017 International Conference on Software Knowledge Information Management and Applications, pp. 1-6, Colombo, Sri Lanka, Dec 2017.

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Last update: 14/01/2019