Laser & Optoelectronics Progress, Volume. 56, Issue 19, 191502(2019)
Multi-Filter Collaborative Tracking Algorithm Based on High-Confidence Updating Strategy
Figures & Tables(13)
Fig. 1. Tracking results and response maps in Box sequence. (a)-(c) 199th, 462nd, and 505th frame images; (d)-(f) 199th, 462nd, and 505th frame response maps
Fig. 5. Results of nine tracking algorithms for nine sequences. (a) Lemming; (b) Human3; (c) Girl2; (d) DragonBaby; (e) MotorRolling; (f) Human7; (g) Skiing; (h) Shaking; (i) Tiger2
Fig. 6. Overall performance of 9 tracking algorithms on OTB-100. (a) Distance precision curve; (b) success rate curve
Fig. 7. Overall analysis of algorithm structure on OTB-100. (a) Distance precision curve; (b) success rate curve
Fig. 8. Overall performance of our algorithm under different updating parameters on OTB-100. (a) Distance precision curve; (b) success rate curve
Fig. 9. Overall performance of 9 tracking algorithms on TC-128. (a) Distance precision curve; (b) success rate curve
Table 1. Attributes and frame numbers of 9 video sequences
View tableTable 1. Attributes and frame numbers of 9 video sequences
Sequence Attributes Frame number DragonBaby SV, OCC, MB, FM, IPR, OPR, OV 113 Human3 SV, OCC, DEF, OPR, BC 1698 MotorRolling IV, SV, MB, FM, IPR, BC, LR 164 Skiing IV, SV, DEF, IPR, OPR 81 Shaking IV,SV, IPR, OPR, BC 365 Tiger2 IV, OCC, DEF, MB, FM, IPR, OPR, OV 365 Girl2 SV, OCC, DEF, MB, OPR 1500 Human7 IV, SV, OCC, DEF, MB, FM 250 Lemming IV, SV, OCC, FM, OPR, OV 1336
Table 2. Distance precision scores of nine algorithms in terms of 11 attributes
View tableTable 2. Distance precision scores of nine algorithms in terms of 11 attributes
Algorithm FM BC MB DEF IV IPR LR OCC OPR OV SV Ours 0.822 0.882 0.833 0.821 0.866 0.855 0.870 0.815 0.850 0.751 0.830 HDT 0.817 0.844 0.789 0.817 0.820 0.844 0.887 0.774 0.805 0.663 0.808 HCF 0.815 0.843 0.804 0.791 0.831 0.864 0.847 0.778 0.816 0.677 0.799 CNN-SVM 0.747 0.776 0.751 0.793 0.795 0.813 0.925 0.730 0.798 0.650 0.787 SRDCF 0.769 0.775 0.767 0.734 0.792 0.745 0.765 0.735 0.742 0.597 0.745 Staple 0.710 0.749 0.699 0.751 0.782 0.768 0.695 0.728 0.738 0.668 0.727 LCT2 0.681 0.734 0.669 0.689 0.746 0.781 0.699 0.682 0.746 0.592 0.681 KCF 0.621 0.713 0.601 0.617 0.719 0.701 0.671 0.630 0.677 0.501 0.633 DSST 0.571 0.704 0.568 0.550 0.722 0.698 0.684 0.603 0.655 0.478 0.649
Table 3. AUC scores of nine algorithms in terms of 11 attributes
View tableTable 3. AUC scores of nine algorithms in terms of 11 attributes
Algorithm FM BC MB DEF IV IPR LR OCC OPR OV SV Ours 0.622 0.637 0.648 0.583 0.638 0.597 0.489 0.605 0.601 0.572 0.583 HDT 0.568 0.578 0.574 0.543 0.535 0.555 0.401 0.528 0.533 0.472 0.486 HCF 0.570 0.585 0.585 0.530 0.549 0.566 0.388 0.532 0.540 0.474 0.485 CNN-SVM 0.546 0.548 0.578 0.547 0.537 0.548 0.379 0.515 0.548 0.488 0.490 SRDCF 0.597 0.583 0.594 0.544 0.613 0.544 0.514 0.559 0.550 0.460 0.561 Staple 0.541 0.561 0.540 0.550 0.593 0.548 0.399 0.542 0.533 0.476 0.520 LCT2 0.507 0.528 0.516 0.482 0.517 0.529 0.330 0.477 0.505 0.446 0.430 KCF 0.459 0.498 0.459 0.436 0.479 0.469 0.290 0.443 0.453 0.394 0.394 DSST 0.458 0.523 0.469 0.427 0.559 0.505 0.382 0.460 0.477 0.386 0.475
Table 4. Comparison of average tracking speeds of 9 tracking algorithmsframe/s
View tableTable 4. Comparison of average tracking speeds of 9 tracking algorithmsframe/s
Tracker Ours HDT HCF CNN-SVM SRDCF Staple LCT2 KCF DSST Average tracking speed 7.9 9.1 13.9 - 4.3 56.3 28.1 317 46.4
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Chaoyi Zhang, Li Peng, Tianhao Jia, Jiwei Wen. Multi-Filter Collaborative Tracking Algorithm Based on High-Confidence Updating Strategy[J]. Laser & Optoelectronics Progress, 2019, 56(19): 191502
Paper Information
Category: Machine Vision
Received: Apr. 2, 2019
Accepted: Apr. 18, 2019
Published Online: Oct. 12, 2019
The Author Email: Wen Jiwei (wjw8143@aliyun.com)
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