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