基于高斯输出约束的上下文相关滤波跟踪

Context-Aware Correlation Filter Tracking Based on Gaussian Output Constraint

为了减少目标跟踪过程中漂移情况的发生,提出一种基于高斯约束输出的上下文相关滤波跟踪算法。该方法通过假设跟踪目标的输出响应服从高斯分布,利用高斯分布的性质可以得出约束输出的具体表现形式;再利用约束输出和相关滤波知识可得到一个可迭代的滤波参数;根据设定的约束条件,对跟踪器中的滤波器进行选择性更新。采用OTB-2013评估基准中的50组视频序列验证了所提算法的有效性,并与其他跟踪算法进行了对比。实验结果表明,所提算法的目标跟踪综合性能明显提升,并且相比于近年来的其他算法也有明显的优势。

Herein,a context-aware correlation filter tracking algorithm based on the Gaussian output constraint(OCCACF)is proposed to reduce the occurrence of drift in the target tracking process.This algorithm assumes that the output response of the tracking target obeys Gaussian distribution.A form of constraint output is derived from the properties of Gaussian distribution and an iterative parameter is obtained using the constraint output and correlation filter knowledge.The filters in this tracker are selectively updated according to setting constraints.The effectiveness of the proposed algorithm is verified using 50video sequences in the OTB-2013evaluation benchmark and the proposed algorithm is compared with other tracking algorithms.Experimental results show that the proposed algorithm can significantly improve the overall performance of target tracking and has obvious advantages than other algorithms that have been proposed in recent years.