Abstract: In order to solve the interference of similar background to object tracker in ground battlefield environment, an object tracking algorithm combining correlation filtering and improved color probability model was proposed. Firstly, based on the traditional color probability model, a color probability model emphasizing foreground was proposed by using the difference between foreground object histogram and background histogram. Then, a spatial penalty matrix was generated according to the correlation filter response confidence and maximum response position. This matrix was used to punish the likelihood probability of background pixel determined by the correlation filter, and the response map of the color probability model was obtained by using the method of integral image. Finally, the response maps obtained by the correlation filter and the color probability model were fused, and the maximum response position of the fusion response map was the central position of the object. The proposed algorithm was compared with 5 state-of-the-art algorithms such as Circulant Structure of tracking-by-detection filters with Kernels (CSK), Kernelized Correlation Filters (KCF), Discriminative Scale Space Tracking (DSST), Scale Adaptive Multiple Feature (SAMF) and Staple in tracking performance. The experimental results on OTB-100 standard dataset show that, the proposed algorithm has the overall accuracy improved by 3.06% to 55.98%, and the success rate improved by 2.24% to 54.97%； and under similar background interference, the proposed algorithm has the accuracy improved by 10.28% to 43.9%, and the success rate improved by 8.3% to 48.29%. The experimental results on 36 battlefield video sequences show that, the proposed algorithm has the overall accuracy improved by 2.2% to 45.98%, and the success rate improved by 3.01% to 58.27%. It can be seen that the proposed algorithm can better deal with the interference of similar background in the ground battlefield environment, and provide more accurate position information for the weapon platform.

Key words: object tracking, correlation filtering, color probability model, likelihood probability, spatial penalty

摘要： 针对地面战场环境下相似背景对目标跟踪器产生的干扰，提出了一种基于相关滤波与改进颜色概率模型的目标跟踪算法。首先，在传统颜色概率模型的基础上，利用前景目标直方图与背景直方图的差异性提出了一种突出前景的颜色概率模型；然后，根据相关滤波器响应置信度和最大响应位置生成空间惩罚矩阵，用该矩阵惩罚相关滤波器判定的背景像素的似然概率，利用积分图的方法得到颜色概率模型响应图；最后，将相关滤波器和颜色概率模型得到的响应图进行融合，融合响应图的最大响应位置即为目标的中心位置。与核循环结构滤波器(CSK)、核相关滤波器(KCF)、判别式尺度空间跟踪（DSST）、SAMF、Staple等5种算法在跟踪性能上进行比较，在OTB-100标准数据集上的结果表明，所提算法的整体精度提高了3.06%至55.98%，成功率提高了2.24%至54.97%；在相似背景干扰下，其精度提高了10.28%至43.9%，成功率提高了8.3%至48.29%。在36段战场视频序列上的结果表明，所提算法的整体精度提高了2.2%至45.98%，成功率提高了3.01%至58.27%。该算法能够更好地应对地面战场环境下相似背景的干扰，为武器平台提供更精确的位置信息。

关键词: 目标跟踪, 相关滤波, 颜色概率模型, 似然概率, 空间惩罚