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摘要:
本文中实现了一种实时鲁棒的目标跟踪方法,提出了新颖的基于目标形状和外观的稠密循环采样方法、循环矩阵和频域空间的能量最小化目标跟踪方法。本文方法总体上减少了需要处理的数据量,尤其是加入了循环矩阵,极大地简化了计算过程,并将目标特征转换到高维频域空间进行了线性表示,最后用高频空间能量最小化的方法实现了更加快速和精准的目标跟踪。通过大量的对比实验表明,本文方法的总体效果较好,在目标朝向变化、场景光照变化、视频抖动、目标尺度模式变化、目标部分遮挡等环境下,较目前效果最好、最新的方法,本文方法在综合的跟踪精度和效率方面更能取得较好的效果。
Abstract:This paper addresses real-time and robust object tracking method. In this paper, dense circulation sampling and frequency domain transform method were used in target tracking processing. This paper proposed energy minimization object tracking method in frequency domain space and put forward the concept of dense circulation sampling to solve object shape changes, appearance changes, object orientation changes, scene illumination changes, video jitter, objective scale changes and object occlusion problems in tracking processing. This method calculates a target by ten adjacent frames and circulation matrix in frequency domain space. This algorithm defines error as an energy function. This method proposed frequency domain energy minimum method firstly. Energy minimization make error between target and ground truth minimize. This algorithm can obtain more precision target results rapidly, so data quantity is sharp decreased. This algorithm use the dense circulation sampling and energy minimization method to implement a stable visual tracking in such situation as target orientation deformation, scene illumination changes, video stabilization, target scale transformation, target part occlusion. Compared with the latest and the best performance methods at present, the proposed method has significantly improved the tracking precision and efficiency.
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表 1 标准数据集中选取的主要挑战序列
Table 1. Main challenge sequence selected from standard database
主要挑战序列 视频序列 场景光照变化 Car4 目标尺度模式变化 Walking2 目标部分遮挡 Singer1 目标变形 Faceocc2 运动模糊 Caviar 快速运动 David2 平面内旋转 CarDark 平面外旋转 Woman 视点变化 Singer1 目标朝向变化 Dudek 复杂背景 David 低分辨率 Faceocc2 表 2 目标跟踪标准数据集中各挑战序列和相关的跟踪结果
Table 2. Each challenge sequence and relative tracking results in standard target tracking database
主要挑战序列 跟踪效果 场景光照变化 快速运动 目标朝向变化 目标尺度模式变化 视频抖动 目标部分遮挡 相似背景干扰 其他情形 表 3 中心位置错误率测试结果
Table 3. Test results of center position error rate
% 测试数据集 本文方法 IVT Frag TLD MTT Struck L1APG LSK MIL DFT LSK OMA ASLA Basketball 2.11 4.5 12.11 22.99 3.21 3.85 3.24 11.32 10.23 1.97 3.55 11.58 1.88 Car4 1.17 1.56 23 31.21 1.54 10.46 1.38 5.64 9.35 1.45 2.56 7.68 1.18 Singer 3.5 3.65 38.46 38.41 9.87 9.62 65.23 24.36 15.34 27.36 9.65 12.54 4.23 Dudek 6.24 11.32 87.99 31.87 17.82 18.32 6.91 17.35 9.65 11.27 11.89 17.65 8.75 Faceocc2 5.22 7.23 15.19 18 6.03 6.55 9.35 16.89 12.36 5.99 17.86 15.28 22.75 CarDark 3.81 12.68 29 10.77 14.32 13.78 4.6 14.19 8.34 5.67 15.25 9.49 3.86 David 3.65 71.45 19.66 63.8 65.09 66.06 66.87 14.57 5.38 62.9 65.85 6.29 3.67 Caviar 3.37 138.65 113.26 59.27 3.38 3.56 123.65 34.75 35.62 134.61 55.21 65.32 146.32 Woman 2.56 1.87 4.62 2.54 2.94 2.99 4.65 6.34 6.54 46.66 5.32 4.59 1.65 MotorRolling 3.15 2.96 61.23 60.66 11.99 11.98 3.52 3.69 56.32 2.1 12.34 10.63 3.16 Shaking 18.88 45.32 35.21 21.45 18.87 18.89 41.32 17.54 18.65 41.36 25.34 19.65 44.8 表 4 平均包围盒覆盖率测试结果
Table 4. Test results of average bounding box error rate
% 测试数据集 本文方法 IVT Frag TLD MTT Struck L1APG LSK MIL DFT LSK OMA ASLA Basketball 0.89 0.78 0.46 0.62 0.98 0.48 0.78 0.67 0.81 0.68 0.59 0.59 0.84 Car4 0.87 0.85 0.18 0.36 0.85 0.87 0.85 0.89 0.83 0.69 0.59 0.71 0.83 Singer 0.79 0.69 0.27 0.49 0.35 0.56 0.27 0.76 0.68 0.95 0.35 0.68 0.68 Dudek 0.91 0.78 0.48 0.67 0.72 0.66 0.78 0.67 0.69 0.58 0.68 0.8 0.75 Faceocc2 0.92 0.74 0.62 0.59 0.87 0.67 0.69 0.38 0.65 0.67 0.67 0.76 0.56 CarDark 0.79 0.49 0.34 0.67 0.46 0.39 0.78 0.67 0.57 0.37 0.65 0.59 0.79 David 0.93 0.26 0.46 0.18 0.23 0.24 0.27 0.68 0.69 0.59 0.58 0.68 0.85 Caviar 0.86 0.2 0.27 0.26 0.19 0.77 0.19 0.69 0.76 0.49 0.32 0.67 0.16 Woman 0.87 0.74 0.67 0.69 0.33 0.71 0.67 0.38 0.34 0.69 0.68 0.67 0.79 MotorRolling 0.85 0.73 0.28 0.27 0.89 0.51 0.76 0.59 0.59 0.69 0.68 0.67 0.85 Shaking 0.83 0.24 0.19 0.26 0.24 0.53 0.29 0.68 0.78 0.67 0.84 0.69 0.23 表 5 精度、鲁棒性、平均重叠率测试结果
Table 5. Test results of accuracy, robustness and average overlap rate
编号 方法 精度/% 鲁棒性/% 平均帧速/(帧·s-1) 平均误差/% 1 本文方法 81.90 8.580 186 7.330 2 IVT 65.60 10.13 20 7.430 3 Frag 43 13.99 615 10.39 4 TLD 48 15.65 38 11.06 5 MTT 46.80 10.13 9 11.40 6 Struck 39.80 16.71 64 11.44 7 L1APG 61.50 6.980 28 11.51 8 LSK 19.74 4.000 65 11.87 9 MIL 37.51 15.10 35 15.24 10 DFT 76.8 14.47 115 15.29 11 LSK 52.65 16.78 86 15.94 12 OMA 23.82 9.670 94 16.74 13 ASLA 48.51 22.51 66 16.80 -
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