Aimed at the problem of occlusion, deformation and illumination in the object tracking, an object tracking method based on multi-feature and local joint sparse representation is proposed within particle filter framework. The color model of the object is established by using HSV space. The texture apparent model of the object is established by using the enhanced center symmetric local binary patterns and represented by the local joint sparse coding. Integrating the color and texture features,the similarities of the object and candidate regions are computed. The object state is estimated by the maximum posterior probability. Whether the object model need to be updated is judged every two frames, which reduces the accumulative errors caused by frequent updates. The proposed method is compared with the other four methods by using visual tracker benchmark data set. Experimental results show that the overall accuracy and success rate of the proposed method is 83.5% and 79.6% respectively. In the case of occlusion, deformation and illumination, the proposed method can track the object accurately and steadily.