Target Tracking Based on Deep Sparse Filtering

To better distinguish tracked target from background in the complex environment, a deep sparse filtering model based on l₂-norm normalization and l₁-norm minimization was designed to learn sparse sample features and the classification by deep learning is carried out. Additionally, a tracking algorithm combining this model with particle filter framework was proposed. Firstly, the deep sparse filtering model is pre-trained layer by layer on the offline training samples set without supervision to get the initial value of weight parameters. Then, during the tracking process, the model is updated online with the labeled samples by supervised fine-tuning to further optimize the weight parameters for better adaptability to target appearance changes. Finally, the full-trained deep sparse filtering model is used to observe candidate areas calculated by particle filter algorithm to determine the tracked target. The experimental results on different video sequences show that the proposed algorithm has the superior performance on tracking precision and robustness under the complex environment.