It is still a very challenging issue to online track arbitrary targets in the unrestricted complex environment.This paper presents an online visual tracking method with improved collaborative appearance model based on model-free framework

solving the problem of most other tracking algorithms with collaborative model

which is unable to effectively select the positive and negative samples.According to the human visual perception rules

object edge information is regarded as the most discriminative feature

on which an edge discriminative appearance model is proposed.In order to remove background interference in likelihood matching space for generative model

a two-stage matching space is put forward via integrating dynamic model

detection module and edge discriminative model.The generative model based on partition strategy is constructed for space and appearance information.The final position and matching coefficient of each sub-block are calculated by mean-shift

as a basis for occlusion handling and model update.Experimental results using challenging public video sequences show the effectiveness and superiority of the proposed method

compared with other state-of-the-art visual tracking approaches.