Research Of Object Tracking Algorithm Based On Superpixel

With the popularization of video surveillance system, computer vision is becoming increasingly familiar to the public. As one of the most important parts of the field of computing vision, object tracking which have been broadly applied in fields like security monitoring system, human computer interaction, driverless cars navigation, has attracted increasing concentrations. Traditional object tracking algorithms have poor effect in robustness and accuracy, in which cannot adapt to complex scene because of its variation and deformation of the visual object. This paper focusing on visual object tracking algorithm make deep research. Based on current superpixel segmentation algorithm and feature space clustering and the Bayesian framework, this paper purposes the robust approach of the visual object tracking which can handle some variation and deformation of the visual object. Main works of this paper include:1. A novel superpixel segmentation algorithm with the anisotropic diffusion filter and the luminance component constraint is proposed. Based on the traditional algorithm of simple linear iterative clustering, the anisotropic filter and the luminance component constraint is introduced into the purposed algorithm which can fit the semantic boundary better and improve the accuracy of the segmentation. Luminance component constraint can also avoid error propagation.2. A constellation appearance model based on superpixel feature pool is purposed. Traditional superpixel feature pool model is divided into a root part and some leaf parts whose relation among parts are defined, which forms a constellation appearance model. The novel constellation appearance model can handle some variation and deformation of object appearance and improve the robustness and accuracy in visual object tracking.3. Based on Bayesian framework, tracking method which is suitable for constellation appearance model is purposed. Bayesian framework is specialized in the constellation appearance model. Confidence map is utilized to characterize the dynamic model and observation model and particle filter is utilized to search the candidate targets. With the suitable online appearance update scheme, the state of the target can be estimated in the tracking process.