[KSCI] Korea Science Citation Index Service

http://dx.doi.org/10.9708/jksci.2018.23.01.041

Study on a Robust Object Tracking Algorithm Based on Improved SURF Method with CamShift

Ahn, Hyochang (Dept. of Smart & PhotoVoltaic Convergence, Far East University)
Shin, In-Kyoung (Dept. of Applied Computer Engineering, Dankook University)

Publication Information

Journal of the Korea Society of Computer and Information / v.23, no.1, 2018 , pp. 41-48 More about this Journal

Abstract

Recently, surveillance systems are widely used, and one of the key technologies in this surveillance system is to recognize and track objects. In order to track a moving object robustly and efficiently in a complex environment, it is necessary to extract the feature points in the interesting object and to track the object using the feature points. In this paper, we propose a method to track interesting objects in real time by eliminating unnecessary information from objects, generating feature point descriptors using only key feature points, and reducing computational complexity for object recognition. Experimental results show that the proposed method is faster and more robust than conventional methods, and can accurately track objects in various environments.

Keywords

Object Tracking; Feature Matching; Object Dectection; SURF; CamShift;

Citations & Related Records

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