Robust and long-term object tracking with an application to vehicles

Recently, intelligent vehicles catch much attention in both academia and industry. The vision-based moving object/vehicle detection and tracking are typically the core techniques for the event and activity analysis and the understanding of the dynamic driving environment in an intelligent vehicle. However, due to the complicated non-stationary environment, most existing vision-based motion tracking algorithms proposed for other simple conditions are not able to consistently track the objects. Therefore, in this paper, we propose a robust and long-term tracking method for intelligent vehicles, in which a set of classifiers are dynamically maintained and sampled for tackling varied challenges. In contrast to previous methods, to increase the diversity, a set of basic classifiers trained sequentially on different small data sets over time is dynamically maintained. The subsets of basic classifiers are independent with each other and can be specified to solve certain different sub-problems occurred in a non-stationary environment. Thus, for every challenge, an optimal classifier can be approximated in a subspace spanned by the selected competitive classifiers, which can address the current problem according to the distribution of the samples and recent performance. As a result, the tracker can efficiently address the various 'concept drift' problems occurred together in a long video sequence. Due to the use of sparse weights for the competitive classifiers, the tracker can keep the balance between the efficiency and the performance. Experimental results show that the tracker yields competitive performance under various challenging environmental conditions and, especially, can overcome several challenges simultaneously.