Abstract:

In order to track the object accurately during a long term in the complicated environment, the scale adaptive multiple model compressive tracking algorithm based on compressive tracking is proposed. Firstly, in order to obtain the adaptive scale of the target, a number of scanning windows with different scales and positions which can be easily computed offline are adopted to the multi-scale model of the target. Secondly, a random projection matrix is used to reduce the dimension of multi-scale image feature space and the computation is reduced. Finally, the tracking task is formulated as a binary classification via a naive Bayes classifier trained by the multiple model classifier with online update in the compressed domain. Experimental results show that the proposed algorithm has good performance in object tracking with changes in scale and appearance. Although the algorithm increases the processing time, still satisfies the need of the real-time requirement.