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Open Access Research

Human tracking with an infrared camera using a curve matching framework

Suk Jin Lee1, Gaurav Shah1, Arka Aloke Bhattacharya2 and Yuichi Motai1*

Author Affiliations

1 Department of Electrical and Computer Engineering, Virginia Commonwealth University, Richmond, VA, USA

2 Department of Electrical Engineering and Computer Science, University of California, Berkley, CA, USA

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EURASIP Journal on Advances in Signal Processing 2012, 2012:99  doi:10.1186/1687-6180-2012-99

Published: 3 May 2012

Abstract

The Kalman filter (KF) has been improved for a mobile robot to human tracking. The proposed algorithm combines a curve matching framework and KF to enhance prediction accuracy of target tracking. Compared to other methods using normal KF, the Curve Matched Kalman Filter (CMKF) method predicts the next movement of the human by taking into account not only his present motion characteristics, but also the previous history of target behavior patterns-the CMKF provides an algorithm that acquires the motion characteristics of a particular human and provides a computationally inexpensive framework of human-tracking system. The proposed method demonstrates an improved target tracking using a heuristic weighted mean of two methods, i.e., the curve matching framework and KF prediction. We have conducted the experimental test in an indoor environment using an infrared camera mounted on a mobile robot. Experimental results validate that the proposed CMKF increases prediction accuracy by more than 30% compared to normal KF when the characteristic patterns of target motion are repeated in the target trajectory.

Keywords:
object tracking; far-infrared imaging; human target; curve matching; Kalman filter