Robust object tracking based on local region sparse appearance model
We propose a robust object tracking algorithm based on local region sparse appearance model in this paper. In this algorithm, the object is divided into several sub regions, and the sparse dictionaries are obtained by clustering in each sub region. Therefore spatial structure information of the object can be captured well, and the change of object appearance can be also resisted effectively. First, the object is divided into many small patches. Then the object is divided into several sub regions according to patch distribution again. The establishment of object dictionary base is based on combination of the dictionaries from all the sub regions, and then space alignment between different parts of the object can be achieved. Meanwhile, noise removal and other operations in the existing sparse reconstruction error maps are performed to retain valuable information. In the updating framework, a novel flexible template set update mechanism is introduced in this paper. In this update mechanism, valuable object samples will be put into the template set. If samples are not valuable, they should not be put into the template set, even when the template set is not full. Then we use patch sparse coefficient histogram of updated templates to extract time domain information of the object in the form of weighted sum. Therefore, it can provide a reliable template basis for obtaining good candidate object. In addition, when tracking result deviates from the actual position of the object, we use a dynamic sub region resampling method based on cosine angle to correct the position deviation timely. Therefore this method can effectively prevent the object from being completely lost. Both qualitative and quantitative evaluations on challenging video sequences demonstrate that the proposed tracking algorithm performs favorably against several state of the art methods.
Neurocomputing