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Object Tracking is one important problem in computer vision and surveillancesystem. The existing models mainly exploit the single-view feature (i.e. color,texture, shape) to solve the problem, failing to describe the objectscomprehensively. In this paper, we solve the problem from multi-viewperspective by leveraging multi-view complementary and latent information, soas to be robust to the partial occlusion and background clutter especially whenthe objects are similar to the target, meanwhile addressing tracking drift.However, one big problem is that multi-view fusion strategy can inevitablyresult tracking into non-efficiency. To this end, we propose to marry ELM(Extreme learning machine) to multi-view fusion to train the global hiddenoutput weight, to effectively exploit the local information from each view.Following this principle, we propose a novel method to obtain the optimalsample as the target object, which avoids tracking drift resulting from noisysamples. Our method is evaluated over 12 challenge image sequences challengedwith different attributes including illumination, occlusion, deformation, etc.,which demonstrates better performance than several state-of-the-art methods interms of effectiveness and robustness.

Marrying Tracking with ELM: A Metric Constraint Guided Multiple Feature Fusion Method