【摘要】 为了解决基于相关滤波器的跟踪方法在长期遮挡或尺度变化情况下跟踪性能降低,对核相关滤波器目标跟踪方法提出了改进.首先将目标区域的方向梯度直方图、颜色名特征进行融合,对目标外观进行建模;然后对目标构建尺度金字塔,求取尺度的最大响应.最后,引入再检测机制,只有当目标的置信度小于阈值时,才利用在线随机蕨分类器重新扫描窗口,获取检测结果.实验结果表明,该文提出的改进算法在目标发生快速运动、遮挡和尺度变化等复杂场景下有较强的鲁棒性.更多还原

【Abstract】 Focusing on the issue that the correlation filter tracking algorithm under the condition of long-term occlusion or scale change has poor performance, the proposed algorithm makes the improvement based on the kernelized correlation filters tracking method. Firstly, the histogram of gradient and color-naming of target area are fused to construct training samples in order to improve the description of the target. Then, the scale is obtained by calculating the maximum response on the multi-scale image pyramid. Finally, the re-detection mechanism is introduced, and only when the response of the target is less than the threshold, the online random fern classifier is trained to re-detect objects. The obtained results of experiment demonstrate that the proposed algorithm is robust in the tracking of fast motion, heavy occlusion, out of view,and other complex scenes.更多还原