Exemplar-based Linear Discriminant Analysis for Robust Object Tracking
This addresses the need for more precise object tracking in computer vision applications, though it appears incremental as it builds on existing tracking-by-detection methods.
The paper tackles the problem of object tracking by proposing an exemplar-based linear discriminant analysis (ELDA) framework that uses specific detectors trained on single object instances rather than general category detectors, achieving robust performance on challenging video sequences.
Tracking-by-detection has become an attractive tracking technique, which treats tracking as a category detection problem. However, the task in tracking is to search for a specific object, rather than an object category as in detection. In this paper, we propose a novel tracking framework based on exemplar detector rather than category detector. The proposed tracker is an ensemble of exemplar-based linear discriminant analysis (ELDA) detectors. Each detector is quite specific and discriminative, because it is trained by a single object instance and massive negatives. To improve its adaptivity, we update both object and background models. Experimental results on several challenging video sequences demonstrate the effectiveness and robustness of our tracking algorithm.