Combating tracking drift : developing robust object tracking methods

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Visual object tracking plays an important role in many computer vision applications, such as video surveillance, unmanned aerial vehicle image processing, human computer interaction and automatic control. This research aims to develop robust object tracking methods, which are capable of tracking general object without the prior knowledge of the target. Tracker drift is one of the most challenging issues in object tracking due to target deformations, illumination variations, abrupt motions, occlusions and background clutters. This thesis focuses on the tracking drift problem, and adopts three main solutions. These include: designing an efficient target shape feature extraction method, comparing target features with metric learning and using the ensemble tracking method to tackle the tracking drift during tracker online update.
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