光学学报, 2020, 40 (3): 0315001, 网络出版: 2020-02-17 复制并引用该论文  本文已被引 8 次  被引通知

图 & 表

图 1. 自适应特征融合过程示意图

Fig. 1. Schematic of adaptive features fusion process

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图 2. 权重调节系数ρ与目标跟踪性能之间的关系

Fig. 2. Relationship between weight adjustment ρ and the target tracking performance

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图 3. 3种目标跟踪算法的距离精度曲线和重叠精度曲线。(a)距离精度;(b)重叠精度

Fig. 3. Distance precision curves and overlap precision curves of three target tracking algorithms. (a) Distance precision; (b) overlap precision

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图 4. 所提算法对4组视频序列进行估计的目标尺度与真实的目标尺度对比。(a) Blurcar2;(b) Dog1;(c)Doll;(d) Carscale

Fig. 4. Comparisons of estimated scale by the proposed algorithm and actual scale on four sequences.(a) Blurcar2; (b) Dog1; (c) Doll; (d) Carscale

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图 5. 不同目标跟踪算法的距离精度曲线和重叠精度曲线。(a)距离精度;(b) 重叠精度

Fig. 5. Distance precision curves and overlap precision curves of different target tracking algorithms. (a) Distance precision; (b) overlap precision

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图 6. 5种算法对视频序列David的目标跟踪结果对比

Fig. 6. Comparison of tracking results among five algorithms on David sequence

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图 7. 5种算法对视频序列Basketball的目标跟踪结果对比

Fig. 7. Comparison of tracking results among five algorithms on Basketball sequence

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图 8. 5种算法对视频序列Carscale的目标跟踪结果对比

Fig. 8. Comparison of tracking results among five algorithms on Carscale sequence

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图 9. 5种算法对视频序列Jogging1的目标跟踪结果对比

Fig. 9. Comparison of tracking results among five algorithms on Jogging1 sequence

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图 10. 5种算法对视频序列Trellis的目标跟踪结果对比

Fig. 10. Comparison of tracking results among five algorithms on Trellis sequence

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图 11. 5种算法对视频序列Soccer的目标跟踪结果对比

Fig. 11. Comparison of tracking results among five algorithms on Trellis sequence

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表 1所提算法对4组存在尺度变化的视频序列的目标跟踪结果

Table1. Tracking results of the proposed algorithm on four scale variation sequences

Sequence Mean ECL Pd /% (ECL=20) Po /% (So=0.5) Blurcar2 3.42 100.0 100.0 Dog1 3.81 100.0 100.0 Doll 2.26 99.3 99.6 Carscale 3.84 100.0 100.0

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表 2排名前10的算法对11种不同属性的评价指标P_d

Table2. P_d scores of the top ten algorithms on eleven attributes

Algorithm IV DEF SV OCC MB FM IPR OPR OV BC LR Proposed 0.780 0.737 0.739 0.761 0.653 0.581 0.704 0.751 0.665 0.714 0.424 DSST 0.730 0.636 0.738 0.692 0.544 0.513 0.768 0.725 0.511 0.694 0.497 KCF 0.657 0.698 0.648 0.695 0.571 0.534 0.691 0.678 0.590 0.676 0.387 Struck 0.558 0.521 0.639 0.564 0.551 0.604 0.617 0.597 0.539 0.585 0.545 SCM 0.594 0.586 0.672 0.640 0.339 0.333 0.597 0.618 0.429 0.578 0.305 CN 0.576 0.607 0.599 0.621 0.551 0.482 0.674 0.645 0.438 0.629 0.408 TLD 0.537 0.512 0.606 0.563 0.518 0.551 0.584 0.596 0.576 0.428 0.349 VTD 0.557 0.501 0.597 0.545 0.375 0.352 0.599 0.620 0.462 0.571 0.168 VTS 0.573 0.487 0.582 0.534 0.375 0.353 0.579 0.604 0.455 0.578 0.187 CXT 0.501 0.422 0.550 0.491 0.509 0.515 0.610 0.574 0.510 0.443 0.371

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表 3排名前10的算法对11种不同属性的评价指标P_o

Table3. P_o of the top ten algorithms on eleven attributes

Algorithm IV DEF SV OCC MB FM IPR OPR OV BC LR Proposed 0.712 0.733 0.721 0.738 0.591 0.532 0.674 0.702 0.672 0.648 0.419 DSST 0.681 0.610 0.640 0.632 0.528 0.503 0.679 0.632 0.512 0.627 0.437 SCM 0.568 0.565 0.635 0.599 0.339 0.335 0.560 0.575 0.449 0.550 0.308 KCF 0.543 0.628 0.474 0.580 0.561 0.523 0.613 0.579 0.610 0.630 0.355 Struck 0.491 0.473 0.471 0.493 0.518 0.567 0.528 0.506 0.550 0.545 0.410 TLD 0.460 0.456 0.494 0.468 0.482 0.473 0.476 0.497 0.516 0.388 0.327 ALSA 0.503 0.456 0.544 0.451 0.281 0.260 0.488 0.494 0.359 0.468 0.163 CN 0.450 0.511 0.421 0.479 0.480 0.437 0.550 0.501 0.458 0.531 0.399 VTS 0.503 0.441 0.453 0.465 0.328 0.325 0.477 0.496 0.508 0.516 0.183 VTD 0.480 0.443 0.460 0.468 0.320 0.319 0.500 0.510 0.491 0.515 0.170

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陈法领, 丁庆海, 常铮, 陈宏宇, 罗海波, 惠斌, 刘云鹏. 自适应特征融合的多尺度核相关滤波目标跟踪[J]. 光学学报, 2020, 40(3): 0315001. Faling Chen, Qinghai Ding, Zheng Chang, Hongyu Chen, Haibo Luo, Bin Hui, Yunpeng Liu. Multi-Scale Kernel Correlation Filter Algorithm for Visual Tracking Based on the Fusion of Adaptive Features[J]. Acta Optica Sinica, 2020, 40(3): 0315001.