Robust discriminative tracking via query-by-bagging

Abstract

Adaptive tracking-by-detection is a popular approach to track arbitrary objects in various situations. Such approaches treat tracking as a classification task and constantly update the object model. The update procedure requires a set of labeled examples, where samples are collected from the last observation, and then labeled. However, these intermediate steps typically follow a set of heuristic rules for labeling and uninformed search in the sample space, which decrease the effectiveness of model update. In this study, we present a framework for adaptive tracking that utilizes active learning for effective sample selection and labeling them. The active sampler employs a committee of randomized-classifiers to select the most informative samples and query their label from an auxiliary detector with a long-term memory. The committee is then updated with the obtained labels. Experiments show that our algorithm outperforms state-of-the-art trackers on various benchmark videos.