针对目标跟踪任务中由于摄像机或目标运动而产生的目标模糊问题, 基于ECO_HC算法提出了方向梯度-色度饱和度直方图(Histogram of Oriented Gradient and Hue Saturation, HOGHS)和Zernike矩特征相结合的运动模糊目标跟踪算法。首先, 结合fHOG和颜色构造了HOGHS特征, 介绍了Zernike矩性质, 并结合HOGHS和Zernike矩实现了目标的特征表达; 然后, 提出了一种兼顾定位精度与鲁棒性的响应图质量评估方法, 并基于该方法实现了HOGHS和Zernike矩特征权重的自适应融合。将本文算法与其他四种先进跟踪算法在OTB-100测评集运动模糊图像序列中进行验证, 本文算法的精确度与成功率分别为0.849与0.827, 帧率达38.4 frame/s, 同等条件下, 本文算法较在VOT-2016上表现优秀的ECO_HC于Pre-20和AUC指标上分别提升了2.3%与2.4%。实验结果表明本文算法能够有效地完成运动模糊目标跟踪任务。

This study presents a motion blur object tracking method which combines the histogram of oriented gradient and hue saturation (HOGHS) with the Zernike moments features based on ECO_HC. This method was proposed to address 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 was proposed that considers both positioning accuracy and robustness. Based on this method, an adaptive fusion strategy that leverages the complementary properties of HOGHS and Zernike moments was implemented. Experiments were performed on the motion blur sequences from the OTB-100 datasheet. Our method was compared to four existing state-of-the-art methods. Results show that the precision and success rate are 0.849 and 0.827, respectively, and the frame rate is 38.4 frame/s. The proposed method outperformed the top performing tracker from VOT-2016, namely, ECO_HC, with a relative gain of 2.3% in Pre-20 and 2.4% in area under the curve. The results show that the proposed method can effectively achieve the goal of motion blur object tracking.