一种复杂背景下红外目标稳定跟踪算法

针对红外单目标在长期跟踪过程中的强背景干扰、遮挡、形变以及目标特征信息减弱等实际问题,提出了一种基于跟踪-学习-检测(Tracking-Learning-Detection,TLD)框架的红外目标稳定跟踪方法。该方法在压缩跟踪算法(Compressive Tracking,CT)的基础上替换广义的类Harr特征为HOG特征,引入互补随机测量矩阵,优化纹理和灰度特征信息的权重,同时引入卡尔曼滤波器记录空间上下文位置信息,以解决CT算法和TLD算法在目标被遮挡时的跟踪失效和全局检索问题。基于TLD算法框架和改进CT算法相结合的红外图像跟踪算法有效地解决了遮挡和强干扰问题,提升了算法的跟踪准确性和长期跟踪稳定性。实验结果表明,本文提出的算法在红外地面环境中能较好地实时稳定跟踪并保持良好的准确性和鲁棒性。

Stable Infrared Target Tracking Algorithm Under Complicated Background

During the long-term tracking process of a single infrared target,many technical problems occur,such as strong background interference,occlusion,deformation,and target feature attenuation.An infrared target-tracking algorithm based on tracking-learning-detection(TLD)was proposed to solve these problems.Based on compressive tracking(CT),generalized Harr-like features were replaced by histograms of oriented gradient features.In our proposed method,a complementary random measurement matrix,which extracted texture and optimized grayscale feature-weights,was introduced.Moreover,a Kalman filter,used to record the space context location information,was adopted.Hence,the tracking failure and global retrieval problem of traditional CT and TLD algorithms can be solved when the target is occluded or deformed.The infrared image-tracking algorithm based on the combination of the TLD algorithm framework and improved CT algorithm effectively solves the problem of occlusion and strong interference and improves the tracking accuracy and long-term tracking stability of the algorithm.Experimental results show that the proposed algorithm can track well in real time and maintain good accuracy and robustness in an infrared ground environment.