视频跟踪中,使用单一特征对目标进行描述难以适应复杂场景的变化,目标的尺度变化、形变、遮挡等因素易导致跟踪失败。为提高跟踪的稳健性,基于核循环结构,提出一种自适应特征融合和模型更新的跟踪方法,并引入尺度更新机制。首先利用目标的灰度特征和局部二值模式特征分别计算滤波响应图,依据响应图的峰值旁瓣比(PSR)自适应地分配权值并加权融合,从而估计目标的最佳位置。然后根据融合后响应图的PSR来判断跟踪质量,据此决定是否更新模型。最后在目标位置周围提取方向梯度直方图特征构建尺度金字塔,训练尺度相关滤波器,用来估计目标的最佳尺度。实验选取标准测试数据集中具有光照变化,遮挡和尺度变化的视频序列进行实验,结果表明,该算法能够实现对目标的稳定跟踪,并且在距离精度和成功率上均优于对比算法。

In video tracking, the use of a single feature to describe the target is difficult to adapt to the changes in complex scenes. Futhermore, the scale change, deformation, occlusion of target and other factors will lead to tracking failure. In order to improve the robustness of tracking, an adaptive feature fusion and model updating tracking algorithm is proposed based on the circulant structure of with kernel, and the scale updating mechanism is also introduced. Firstly, the response maps are calculated using the gray and local binary pattern features of the target respectively and fused by the weights assigned according to the peak to sidelobe ratio(PSR), and the best location is estimated. The PSR of the fused response map is also used to judge the tracking quality to decide whether to update the model. Finally, according to the scale pyramid constructed with the histograms of oriented gradients features extracted around the target location,the scale correlation filter is trained to estimate the optimal scale of the target. The experiment selects the sequences with illumination variations, occlusion and scale changes from the visual tracker benchmark datasets. The results show that the proposed algorithm can track the target robustly in complex scenes, and the distance precision and success rate are also superior to the compared algorithms.