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

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.

Research highlights
► Kalman filter improves low-resolution color-based visual tracking.
► Performance of Kalman filter depends on optimization of state-space model.
► Optimal state-space model is identified by maximum likelihood estimator.
► Optimal state-space model incorporates quantization effects on tracking data.