Note: Low-resolution color-based visual tracking with state-space model identification

Authors:
Xin Lu;Kiyoshi Nishiyama
Affiliations:
Department of Electrical Engineering and Computer Science, Faculty of Engineering, Iwate University, 4-3-5, Ueda, Morioka 020-8551, Japan;Department of Electrical Engineering and Computer Science, Faculty of Engineering, Iwate University, 4-3-5, Ueda, Morioka 020-8551, Japan
Venue:
Computer Vision and Image Understanding
Year:
2010

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Abstract

A novel tracking method is proposed to resolve the poor performance of color-based tracker in low-resolution vision. The proposed method integrates vector autoregression (VAR) with a conceptual frame of state-space model (SSM) to achieve an appropriate model that clearly describes the relation between high-resolution tracking results (states) and corresponding low-resolution tracking results (observations). Here, the parameters of SSM are calculated by the maximum likelihood (ML) estimator to optimize the SSM and minimize its model error. By using the Kalman filter, known as an effective filter of SSM, to estimate the states of the tracked object from its incomplete observations, it is observed that the estimated states are closer to their actual values than their observations or estimates by other unoptimized SSMs. Therefore, the proposed method can be used to improve low-resolution tracking results. Moreover, it can decrease computational complexity and save on processing time.