相关滤波器由于近年来在实时准确地跟踪视频信号方面展示出了优异的性能,为此引起了很多学者的关注。但在应对复杂场景外观变化较大的情况下,滤波器的跟踪效果易不稳定,估计尺度变化能力也有待改进,模型更新过程易引入不可信样本,不利于精确跟踪目标。为此,采用颜色直方图模型和相关滤波判别模型相结合的方式对跟踪目标进行精确定位,再从预测位置处提取尺度样本训练核尺度相关器。针对模型更新过程中可能引入干扰信息的问题,选择当响应置信度达到一定阈值时,对模型进行更新。在跟踪数据集(OTB-2015)中所提方法表现良好,成功率为0.694,精确度为0.794。

Correlation filters have demonsrtated excellent performance in real-time and accurate tracking of video signals in recent years, which have attracted the attention of many scholars. However, when the appearance of complex scenes changes greatly, the tracking effect of the filter is easy to be unstable, and the ability to estimate scale changes needs improvement. The model update process easily introduces untrustworthy samples, which is not conducive to track the target accurately. Therefore, a combination of a color histogram model and correlation filtering-discrimination model is used to track objects accurately, and then scale samples are extracted from the predicted position to train the kernel scale correlator. Considering the problem of interference information getting introduced in the model update process, the model is updated when the response confidence attains a certain threshold. The proposed algorithm in the tracking data set (OTB-2015) performed well with a success rate and a precision score of 0.694 and 0.794, respectively.