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Developing an effective target appearance model is a challenging task due to the influence of factors such as partial occlusion, illumination variations, fast motion, etc. Existing appearance models usually utilize the tracking results from previous frames as target templates upon which the target appearance model is built by linear combinations of the templates. With such kind of representation, visual tracking is not robust when drastic appearance variations occur. We propose a simple but effective tracking algorithm with a novel appearance model in a particle filter framework. A target candidate is represented by the convex combination of a set of target templates. Additionally, the distance between a target candidate and the templates is measured using the EMD. Experimental results on challenging video sequences against state-of-the-art algorithms demonstrate the robustness and effectiveness of the proposed tracking algorithm.

Gaussian Mixture Model (GMM) and its variations process images by per pixel, so they may be corrupted by noises and the computational cost is high. In this paper, we propose a robust moving object detection algorithm with a background dictionary learning. To do this, we first divide an image into multiple image patches that have the same sizes. Each patch is the object or background. Then, A background dictionary is learnt for each patch. The similarity between a patch and the background dictionary is measured, upon which a patch is distinguished between the object and the background. Additionally, in order to adapt the dynamic contexts across in a video sequence, a robust background dictionary updating scheme is proposed. Experimental results demonstrate the effectiveness and robustness of the proposed detection algorithm.