Efficient Distraction Detection in Surveillance Video using Approximate Counting over Decomposed Micro-streams

Avi Bleiweiss

Abstract

Mining techniques of infinite data streams often store synoptic information about the most recently observed data elements. Motivated by space efficient solutions, our work exploits approximate counting over a fixed-size sliding window to detect distraction events in video. We propose a model that transforms inline the incoming video sequence to an orthogonal set of thousands of binary micro-streams, and for each of the bit streams we estimate at every timestamp the count of number-of-ones in a preceding sub-window interval. On window bound frames, we further extract a compact feature representation of a bag of count-of-1’s occurrences to facilitate effective query of transitive similarity samples. Despite its simplicity, our prototype demonstrates robust knowledge discovery to support the intuition of a context-neutral window summary. To evaluate our system, we use real-world scenarios from a video surveillance online-repository.

References

  1. Arasu, A. and Manku, G. S. (2004). Approximate counts and quantiles over sliding windows. In Principles of Database Systems (PODS), pages 286-296, Paris, France.
  2. Baeza-Yates, R. and Ribeiro-Neto, B., editors (1999). Modern Information Retrieval. ACM Press Series/Addison Wesley, Essex, UK.
  3. Chen, X. J., Zhan, Y. Z., Ke, J., and Chen, X. B. (2015). Complex video event detection via pairwise fusion of trajectory and multi-label hypergraphs. Multimedia Tools and Applications, pages 1-22.
  4. Datar, M., Gionis, A., Indyk, P., and Motwani, R. (2002). Maintaining stream statistics over sliding windows. SIAM Journal of Computing, 31(6):1794-1813.
  5. Efrat, A., Fan, Q., and Venkatasubramanian, S. (2007). Curve matching, time warping, and light fields: New algorithms for computing similarity between curves. Mathematical Imaging and Vision, 27(3):203-216.
  6. Jaechul, K. and Grauman, K. (2009). Observe locally, infer globally: A space-time MRF for detecting abnormal activities with incremental updates. In Computer Vision and Pattern Recognition (CVPR), pages 2921- 2928, Miami, FL.
  7. Lee, L. K. and Ting, H. F. (2006). Maintaining significant stream statistics over sliding windows. In Discrete Algorithm (SODA), pages 724-732, Miami, FL.
  8. Lee, S. C. and Nevatia, R. (2014). Hierarchical abnormal event detection by real time and semi-real time multitasking video surveillance system. Machine Vision and Applications, 25(1):133-143.
  9. Leskovec, J., Rajaraman, A., and Ullman, J. D., editors (2014). Mining of Massive Datasets. Cambridge University Press, New York, NY.
  10. Li, H., Zhang, Y., Yang, M., Men, Y., and Chao, H. (2014). A rapid abnormal event detection method for surveillance video based on a novel feature in compressed domain of HEVC. In Multimedia and Expo (ICME), pages 1-6, Chengdu, China.
  11. Salton, G., Wong, A., and Yang, C. S. (1975). A Vector Space Model for Automatic Indexing. Communications of the ACM, 18(11):613-620.
  12. Shimada, A., Nagahara, H., and Taniguchi, R. I. (2015). Change detection on light field for active video surveillance. In Advanced Video and Signal Based Surveillance (AVSS), pages 1-6, Karlsruhe, Germany.
  13. Vezanni, R. and Cucchiara, R. (2010). Video surveillance online repository (ViSOR): an integrated framework. Journal of Multimedia Tools and Applications, 50(2):359-380.
  14. ViSOR (2010). Video surveillance online repository. http://www.openvisor.org .
  15. Xiang, T. and Gong, S. (2008). Video behavior profiling for anomaly detection. Pattern Analysis and Machine Intelligence, 30(5):893-908.
  16. Zhong, H., Shi, J., and Visontai, M. (2004). Detecting unusual activity in video. In Computer Vision and Pattern Recognition (CVPR), pages 819-826, Washington, DC.
Download


Paper Citation


in Harvard Style

Bleiweiss A. (2016). Efficient Distraction Detection in Surveillance Video using Approximate Counting over Decomposed Micro-streams . In Proceedings of the 8th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management - Volume 1: KDIR, (IC3K 2016) ISBN 978-989-758-203-5, pages 327-335. DOI: 10.5220/0006067103270335


in Bibtex Style

@conference{kdir16,
author={Avi Bleiweiss},
title={Efficient Distraction Detection in Surveillance Video using Approximate Counting over Decomposed Micro-streams},
booktitle={Proceedings of the 8th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management - Volume 1: KDIR, (IC3K 2016)},
year={2016},
pages={327-335},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006067103270335},
isbn={978-989-758-203-5},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 8th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management - Volume 1: KDIR, (IC3K 2016)
TI - Efficient Distraction Detection in Surveillance Video using Approximate Counting over Decomposed Micro-streams
SN - 978-989-758-203-5
AU - Bleiweiss A.
PY - 2016
SP - 327
EP - 335
DO - 10.5220/0006067103270335