基于通道权重融合特征的目标跟踪算法

目标跟踪是机器视觉领域的一个研究热点, 如何提高复杂场景下的跟踪水平是一个挑战性的问题. 以往的研究表明, 如何有效使用特征是实现跟踪的关键. 因此, 提出一种基于通道融合特征的目标跟踪算法. 该方法基于多通道相关滤波框架, 引入特征通道权重, 根据通道对响应值的贡献度调整权重, 从而构建实时特征组合. 该算法能够快速捕捉目标状态变化, 有效跟踪目标. 为了验证算法跟踪的有效性, 我们在公开数据集OTB-2015上测试算法性能, 并与多种跟踪算法进行比较. 实验结果显示, 该算法在跟踪精度、成功率上都取得较好的结果, 整体性能优于对比算法.

Object Tracking Algorithm with Fusion Feature Based on Channel Weight

Target tracking is a research hotspot in the field of machine vision. How to improve the tracking level in complex scenarios is a challenging problem. Previous studies have shown that how to use features effectively is the key to tracking. Therefore, a target tracking algorithm based on channel fusion features is proposed. Based on the multi-channel correlation filtering framework, the method introduces the feature channel weight, adjusts the weight according to the contribution of the channel to the response value, and then constructs the real-time feature combination. The algorithm can capture the state change of the target quickly and track the target effectively. In order to verify the effectiveness of the algorithm tracking, we test the algorithm performance on the open dataset OTB2015 and compare it with a variety of tracking algorithms. Experimental results show that the algorithm has sound tracking accuracy and success rate, and the overall performance is better than the compared algorithm.