Target tracking from infrared imagery via an improved appearance model

Moving target tracking from the infrared images is a challenging task due to the targets' unstable appearance, complex background clutters and a limited number of pixels for each target. To address this challenge, we propose an infrared target tracking method that combines the Sparsity-based Generative Model (SGM) and Guided Filter-based Discriminative Classifier (GFDC) with the particle filter. The SGM model extracts an effective template histogram, which considers the spatial information of each image patch. In the GFDC model, the guided filter uses the structural information of the guidance image in order to smooth the input image, while preserving the edges. This enhances the information of templates while suppressing the noise in the image data. The Bayesian classification is then applied to take the background into consideration when the model is fused. The online update scheme not only considers appearance variations but also relieves the tracking deviation problem. At last, we utilize the joint confidence values to measure the candidate templates for the target position estimation. Experiments based on infrared video sequences have validated the superior tracking performance of the proposed method.