Hoghs And Zernike Moments Features-Based Motion-Blurred Object Tracking

Motion-blurred object tracking method integrating Histogram of Oriented Gradients and Hue Saturation (HOGHS) with Zernike moments features and based on ECO_HC tracker was proposed in this study to deal with object blur caused by the motion of the camera or the object itself. HOGHS was constructed by integrating fHOG with color features, and the properties of Zernike moments were introduced. The object was represented by combining HOGHS and Zernike moments. Furthermore, a novel quality evaluation method of response map considering both positioning accuracy and robustness was proposed and based on the method an adaptive fusion strategy utilizing the complementary properties of HOGHS and Zernike moments was implemented. Experiments were performed on the motion blur sequences from OTB-100 datasheet. Our method was compared with four other state-of-the-art methods. The precision as well as success rate were 0.849 and 0.827, respectively. Speed was 38.4 FPS. It is superior to VOT-2016's excellent tracker ECO_HC, with relative gains of 2.3% for Pre-20 and 2.4% for AUC. The results show that the proposed method can effectively achieve the objective of motion-blurred object tracking.