Object tracking based on depth sparse learning

To address the problem of low precision and pour robustness, this paper will introduce deep learning into visual tracking field and propose an object tracking algorithm based on depth sparse learning. The algorithm first constructs a deep network based on the auto-encoder and then adding the sparsity constraint in the deep network to sparse connection matrix between the hidden layer and the output layer. As a result, the algorithm optimizes the parameters of the deep network and improve its efficiency. It means that more essential features of the target will be extracted with this network. In the prediction of the target, the algorithm introduces the difference between target and background into the particle filter and design a scoring device based on support vector machine, so that particle performance is enhanced and the risk of drift in the process of tracking target is reduced. Experiments on different video sequences have been carried out for many times. According to the results of experiments, we can come to a conclusion that our algorithm has higher accuracy and better robustness, especially under the circumstance of illumination change, similar background and occlusion.