Transfer learning based compressive tracking

Shu Tian, Xu-Cheng Yin, Xi Xu, Hong-Wei Hao

Abstract: Although existing online tracking algorithms can solve the problems of scene illumination changes, partial or full object occlusions, and pose variation, there are still two weaknesses, inadequacy of training data and drift problem. Considering these, Compressive Tracking algorithm (CT) [1] extracts features from compressed domain, and classified object and background via a naive Bayes classier with online update. To further solve the problems of drift and inadequacy of training data, we introduce transfer learning into CT to take full advantage of prior information and propose a self-traininglike transfer learning algorithm. It selects training samples from samples collection to update classifier by the conduction of the classifier constructed at first frame. Eventually we introduce self-training-like transfer learning algorithm into CT to construct a novel tracking algorithm called Transfer Learning based Compressive Tracking (TLCT). Experimental results on 17 publicly available challenging sequences have shown the effectiveness and robustness of our algorithm.