Adaptive Visual Tracking with Minimum Uncertainty Gap Estimation

Abstract

A novel tracking algorithm is proposed, which robustly tracks a target by finding the state that minimizes the likelihood uncertainty. Likelihood uncertainty is estimated by determining the gap between the lower and upper bounds of likelihood. By minimizing the gap between the two bounds, the proposed method identifies the confident and reliable state of the target. In this study, the state that provides the Minimum Uncertainty Gap (MUG) between likelihood bounds is shown to be more reliable than the state that provides the maximum likelihood only, especially when severe illumination changes, occlusions, and pose variations occur. A rigorous derivation of the lower and upper bounds of the likelihood for the visual tracking problem is provided to address this issue. Additionally, an efficient inference algorithm that uses Interacting Markov Chain Monte Carlo (IMCMC) approach is presented to find the best state that maximizes the average of the lower and upper bounds of likelihood while minimizing the gap between the two bounds. We extend our method to update the target model adaptively. To update the model, the current observation is combined with a previous target model with the adaptive weight, which is calculated according to the goodness of the current observation. The goodness of the observation is measured using the proposed uncertainty gap estimation of likelihood. Experimental results demonstrate that the proposed method robustly tracks the target in realistic videos and outperforms conventional tracking methods.