摘要： 针对复杂背景下采用单个传感器跟踪性能差的问题,提出一种基于稀疏表示和L1-APG的红外可见目标融合跟踪算法。对红外与可见光目标分别进行稀疏表示,并以它们的联合重构误差最小为目标构建最优化问题,采用L1-APG算法求解该优化问题,运用最小误差边界约束条件降低算法的计算复杂度,并实现红外与可见光目标模板的动态更新。测试结果表明,该融合跟踪算法在处理目标遮挡、目标旋转移动、目标交叉和夜间光照不均等方面要优于传统的单源跟踪方法。

关键词: 融合跟踪, 红外-可见光目标, 稀疏表示, 1范数加速近邻梯度, 粒子滤波

Abstract: Aiming at the poor tracking performance problem of using single sensor in complex background,a fusion tracking algorithm for infrared-visible targets based on sparse representation and L1-APG is proposed.Firstly,the target models of infrared and visible light are built respectively by using sparse representation method,and the optimization problem is constructed by minimizing their joint reconstruction error.Secondly,the optimization problem is solved by employing L1-APG algorithm.Finally,the computational complexity of the algorithm is further reduced by using the minimum error boundary constraint,and dynamic updates of the infrared and visible target templates are achieved.Test results of infrared and visible image sequences demonstrate that the proposed algorithm performs better than the traditional single source tracking algorithm in dealing with target occlusion,target rotation,target intersection,and illumination change.

Key words: fusion tracking, infrared-visible light target, sparse representation, L1-Accelerated Proximal Gradient(L1-APG), particle filtering

中图分类号: 

• TP391.41