Vision-Based Vehicle Detection and Tracking Using Online Learning 


Vol. 39,  No. 1, pp. 1-11, Jan.  2014


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  Abstract

In this paper we propose a system for vehicle detection and tracking which has the ability to learn on-line appearance changes of vehicles being tracked. The proposed system uses feature-based tracking method to estimate rapidly and robustly the motion of the newly detected vehicles between consecutive frames. Simultaneously, the system trains an online vehicle detector for the tracked vehicles. If the tracker fails, it is re-initialized by the detection of the online vehicle detector. An improved vehicle appearance model update rule is presented to increase a tracking performance and a speed of the proposed system. Performance of the proposed system is evaluated on the dataset acquired on various driving environment. In particular, the experimental results proved that the performance of the vehicle tracking is significantly improved under bad conditions such as entering a tunnel and passing rain.

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  Cite this article

[IEEE Style]

S. Gil and G. Kim, "Vision-Based Vehicle Detection and Tracking Using Online Learning," The Journal of Korean Institute of Communications and Information Sciences, vol. 39, no. 1, pp. 1-11, 2014. DOI: .

[ACM Style]

Sung-Ho Gil and Gyeong-Hwan Kim. 2014. Vision-Based Vehicle Detection and Tracking Using Online Learning. The Journal of Korean Institute of Communications and Information Sciences, 39, 1, (2014), 1-11. DOI: .

[KICS Style]

Sung-Ho Gil and Gyeong-Hwan Kim, "Vision-Based Vehicle Detection and Tracking Using Online Learning," The Journal of Korean Institute of Communications and Information Sciences, vol. 39, no. 1, pp. 1-11, 1. 2014.

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pISSN : 1226 - 4717
eISSN : 2287 - 3880