A new tracker based on the multiple instance learning framework is proposed in this work, which introduces modified distribution fields features and a temporal fusion framework. The new distribution field features can describe the spatial information of the object more efficiently, and gain robustness to motion blur, minor occlusion and deformation of the object. Our temporal information fusion framework can contain the previous information of the object, and respond to the appearance variation of the object simultaneously, which improves the tracker's ability of recovering from tracking outliers. This new algorithm obtains a better performance on several test sequences compared with other state-of-the-art methods, and can track the object stably under various complicated situations.