Kernelized temporal locality learning for real-time visual tracking

Abstract

Linear representation-based methods play an important role in the development of the target appearance modeling in visual tracking. However, such linear representation scheme cannot accurately depict the nonlinearly distributed appearance variations of the target, which often leads to unreliable tracking results. To fix this issue, we introduce the kernel method into the locality-constrained linear coding algorithm to comprehensively exploit its nonlinear representation ability. Further, to fully consider the temporal correlation between neighboring frames, we develop a point-to-set distance metric with L2, 1 norm as the temporal smoothness constraint, which aims to guarantee that the object between the two consecutive frames should be represented by the similar dictionaries temporally. Experimental results on Object Tracking Benchmark show that the proposed tracker achieves promising performance compared with other state-of-the-art methods.