提出了一种通过置信度判别将模型更新方法和尺度变化加入相关滤波器中的目标跟踪算法。在跟踪过程中常会遇到较多遮挡及相似干扰的情况,如果持续更新模型参数极易导致误跟或跟丢,因此采用置信度参数定性地判别跟踪质量,置信度低时停止更新,防止引入误差,提高正确率。确保准确跟踪后,再对尺度大小进行检测和更新,提出了较为快捷的尺度更新方式,简化冗余代码,使跟踪更精确的同时降低时间代价。实验证明,本文算法在精度和正确率方面分别比原算法提升了38%和33%,且性能优于几种现有算法,应对遮挡和尺度变化的情况具有更高的稳健性。

A target tracking algorithm via confidence evaluation is proposed to add the scale and model updating method to the correlation filter. In the process of tracking, there are many cases of occlusion and similar interference. If the model parameter is continuously updated, it can easily lead to false track and target loss. Therefore, the quality of tracking is judged qualitatively by the confidence. When the confidence is low, we stop updating to prevent the introduction of error and improve the accuracy. After ensuring the tracking is correct, we can detect and update the scale size. We propose a faster scale updating method with redundant code simplified, and make the tracking more accurate and with lower time cost. The experimental results show that the proposed algorithm improves the precision and success rate by 38% and 33%, respectively compared to the original algorithm, it has better performance than several existing algorithms, and is more robust to cope with occlusion and scaling.