Multi-person tracking using SURF and background subtraction for surveillance


Juhee Yu, Kyoung-Mi Lee, Journal of Information Processing Systems
Vol. 15, No. 2, pp. 344-358, Apr. 2019
10.3745/JIPS.02.0109
Keywords: Background subtraction, Feature Detection, SURF, Tracking, video surveillance
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Abstract

Surveillance cameras have installed in many places because security and safety is becoming important in modern society. Through surveillance cameras installed, we can deal with troubles and prevent accidents. However, watching surveillance videos and judging the accidental situations is very labor-intensive. So now, the need for research to analyze surveillance videos is growing. This study proposes an algorithm to track multiple persons using SURF and background subtraction. While the SURF algorithm, as a person-tracking algorithm, is robust to scaling, rotating and different viewpoints, SURF makes tracking errors with sudden changes in videos. To resolve such tracking errors, we combined SURF with a background subtraction algorithm and showed that the proposed approach increased the tracking accuracy. In addition, the background subtraction algorithm can detect persons in videos, and SURF can initialize tracking targets with these detected persons, and thus the proposed algorithm can automatically detect the enter/exit of persons.


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Cite this article
[APA Style]
Juhee Yu and Kyoung-Mi Lee (2019). Multi-person tracking using SURF and background subtraction for surveillance. Journal of Information Processing Systems, 15(2), 344-358. DOI: 10.3745/JIPS.02.0109.

[IEEE Style]
J. Yu and K. Lee, "Multi-person tracking using SURF and background subtraction for surveillance," Journal of Information Processing Systems, vol. 15, no. 2, pp. 344-358, 2019. DOI: 10.3745/JIPS.02.0109.

[ACM Style]
Juhee Yu and Kyoung-Mi Lee. 2019. Multi-person tracking using SURF and background subtraction for surveillance. Journal of Information Processing Systems, 15, 2, (2019), 344-358. DOI: 10.3745/JIPS.02.0109.