Acta Optica Sinica, Volume. 40, Issue 3, 0315001(2020)
Multi-Scale Kernel Correlation Filter Algorithm for Visual Tracking Based on the Fusion of Adaptive Features
Figures & Tables(14)
Fig. 2. Relationship between weight adjustment ρ and the target tracking performance
Fig. 3. Distance precision curves and overlap precision curves of three target tracking algorithms. (a) Distance precision; (b) overlap precision
Fig. 4. Comparisons of estimated scale by the proposed algorithm and actual scale on four sequences.(a) Blurcar2; (b) Dog1; (c) Doll; (d) Carscale
Fig. 5. Distance precision curves and overlap precision curves of different target tracking algorithms. (a) Distance precision; (b) overlap precision
Fig. 7. Comparison of tracking results among five algorithms on Basketball sequence
Fig. 8. Comparison of tracking results among five algorithms on Carscale sequence
Fig. 9. Comparison of tracking results among five algorithms on Jogging1 sequence
Fig. 10. Comparison of tracking results among five algorithms on Trellis sequence
Fig. 11. Comparison of tracking results among five algorithms on Trellis sequence
Table 1. Tracking results of the proposed algorithm on four scale variation sequences
View tableTable 1. 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
Table 2. Pd scores of the top ten algorithms on eleven attributes
View tableTable 2. Pd 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
Table 3. Po of the top ten algorithms on eleven attributes
View tableTable 3. Po 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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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
Paper Information
Category: Machine Vision
Received: Jul. 25, 2019
Accepted: Sep. 29, 2019
Published Online: Feb. 17, 2020
The Author Email: Faling Chen (chfling@sia.cn)
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