中文摘要：

为了克服目标尺度变化导致的跟踪失败问题,提出了一种快速多特征金字塔的尺度目标跟踪算法。该算法融合了梯度特征和颜色特征,提高了特征表征的维度,以便获得更多的目标表征信息;同时利用多尺度特征金字塔快速地近似相邻尺度特征,得到不同尺度模板,从而平衡了由于特征维度上升带来的计算时间开销,并保证了近似的准确性;在相关滤波框架下,综合不同尺度模板的跟踪结果,实现对目标位置和尺度的准确估计。选取4个具有尺度变化、光照变化和背景干扰的典型场景视频序列进行仿真实验,结果表明,与传统的尺度自适应核跟踪算法相比,提出的跟踪算法能够很好地适应外部环境变化,实现对尺度目标的鲁棒跟踪,同时在中心位置误差、覆盖率、精确度和成功率4个指标上优于对比算法。

英文摘要：

A fast scale estimation algorithm for visual tracking with feature integration is proposed to solve tracking failure from object scale changes. The gradient feature and color feature are integrated to obtain more object representation information with the increasing feature dimensions, then a fast multi-scale feature pyramid method is used to approximate the adjacent scale features to get templates in different scales, thus it is possible to balance the computation cost due to the increasing feature dimensions without accuracy loss after approximation. Combining tracking results of multi-scale templates, the object location and scale are estimated accurately by the proposed algorithm in the framework of correlation tracking 4 representative video sequences with scale changes, and illumination variations and background clusters are chosen to simulate. The experiments indicate that the proposed algorithm well adapts to environmental variations and outperforms the traditional scale-adaptive kernel correlation tracking schemes in center location error, overlap ratio, precision and success rate.