Robust Visual Tracking Using Exemplar-Based Detectors

Abstract

Tracking by detection has become an attractive tracking technique, which treats tracking as an object detection problem and trains a detector to separate the target object from the background in each frame. While this strategy is effective to some extent, we argue that the task in tracking should be searching for a specific object instance instead of an object category. Based on this viewpoint, a novel framework based on object exemplar detectors is proposed for visual tracking. To build a specific and discriminative model to separate the object instance from the background, the proposed method trains an exemplar-based linear discriminant analysis (ELDA) classifier for the object exemplar, using the current tracked instance as the positive sample and massive negative samples obtained both offline and online. To improve the trackers' adaptivity, we use an ensemble of the above ELDA detectors and update them during the tracking to cover the variation in object appearance. Extensive experimental results on a large benchmark data set show that the proposed method outperforms many state-of-the-art trackers, demonstrating the effectiveness and robustness of the ELDA tracker.