Sigma Set Based Implicit Online Learning for Object Tracking

Abstract

This letter presents a novel object tracking approach within the Bayesian inference framework through implicit online learning. In our approach, the target is represented by multiple patches, each of which is encoded by a powerful and efficient region descriptor called Sigma set. To model each target patch, we propose to utilize the online one-class support vector machine algorithm, named Implicit online Learning with Kernels Model (ILKM). ILKM is simple, efficient, and capable of learning a robust online target predictor in the presence of appearance changes. Responses of ILKMs related to multiple target patches are fused by an arbitrator with an inference of possible partial occlusions, to make the decision and trigger the model update. Experimental results demonstrate that the proposed tracking approach is effective and efficient in ever-changing and cluttered scenes.