基于相关滤波的尺度自适应目标跟踪

针对视频目标跟踪中的尺度问题，提出了一种基于相关滤波的尺度自适应目标跟踪方法。首先利用核相关滤波获得目标的中心位置，然后将目标均分为四个子块，通过计算找出子块中心的最大响应位置，最后根据前后两帧目标子块中心位置的相对变化计算出尺度的伸缩系数，进而计算出目标尺度。在具有尺度变化的公开数据集上对本文方法进行测试，并和多种跟踪方法作对比，实验结果表明，该方法将尺度的计算问题转化为对子块中心的定位，其平均跟踪性能优于其它方法，验证了方法的有效性。

Scale-adaptive tracking based on kernelized correlation filter

Robust scale calculation is a challenging problem in visual tracking. Most existing trackers fail to handle large scale variations in complex videos. To address this issue, this paper proposed a robust and efficient scale-adaptive tracker in tracking-by-detection framework, which divided the target into four patches and computed the scale factor by finding the maximum response position of each patch via kernelized correlation filter. With this method, the scale computation was transformed into locating the centers of the patches. Experiments were performed on several challenging sequences with scale variations in the recent benchmark evaluation. And the results show that our method outperforms state-of-the-art tracking methods while operating in real-time.