Acta Optica Sinica, Volume. 38, Issue 7, 0715002(2018)
A Scale Adapted Tracking Algorithm Based on Kernelized Correlation
Figures & Tables(10)
Fig. 4. (a) Precision plots and (b) success plots of 28 sequences with scale variations
Table 1. Scale adapted tracking algorithm based on kernelized correlation
View tableTable 1. Scale adapted tracking algorithm based on kernelized correlation
Input: Image sequence: I 1,I 2, …,In . Initial target position:p 0=(x 0,y 0), and initial target scale:s 0=(w 0,h 0)Output: The estimated position of target: pt= (xt ,yt ), and estimated scale:st= (wt ,ht )for t =1,2,3,…,n , do:1 Locate the ROI area in frame # t centered atpt -1 with the scale ofst -1;2 Crop out the ROI image and resize to the size of sample template; 3 Extract the HOG and color features; 4 Learn the kernelized correlation response map using Eq.(10); 5 Locate the center of the target pt in frame #t using Eq.(12);6 Obtain the multi-scale sample image Is= {Is 1,Is 2,…,IsS } in frame #t based onpt andst- 1;7 Build scale filters by extracting fusion features from the above multi-scale image; 8 Compute the kernelized correlation response score using Eq.(16); 9 Estimate the optimal scale st of target in the frame #t using Eq.(17) and Eq.(18);10 Update the translation filters using Eq.(19) and Eq.(20); 11 Update the scale filters using Eq.(21). Until End of the image sequence.
Table 2. Center location errors and overlap rates of 12 sequences
View tableTable 2. Center location errors and overlap rates of 12 sequences
Sequence Proposed CNT DSST CST SST KCF NRMLC DLT CN car scale 7.3(75) 23.4(57) 18.8(46) 12.5(48) 87(59) 16.1(47) 10.4(74) 25.5(61) 25.2(43) fleet face 22(69) 24.7(60) 28.3(58) 67.2(54) 60.8(67) 26.4(63) 96.9(44) 27.5(56) 126.2(26) dog1 3.8(99) 6.8(95) 4.6(66) 4.8(67) 4.9(89) 4.1(64) 11.6(91) 4.4(92) 3.5(68) singer2 7.2(100) 6.8(100) 8.2(100) 7.4(100) 175.2(4) 10.2(100) 195.4(3) 173.0(3) 167.1(4) skating1 6.2(53) 6.7(60) 6.8(52) 7.9(58) 8.8(62) 7.7(50) 15(52) 52.9(49) 8.0(52) shaking 7.7(74) 74.1(5) 8(73) 5.7(72) 8.1(75) 113.2(4) 109.4(3) - 15.1(60) sylvester 8.7(75) 10.7(62) 14.8(63) 12.9(68) 11.2(63) 13.3(67) 24.5(49) 10.9(51) 9.5(68) basketball 5.3(78) 534.1(6) 111.6(28) 23.5(57) 106.0(22) 8.1(67) 60.0(7) 12.0(51) 9.3(63) tiger1 12(68) 94.2(15) 19.5(63) 11.2(74) 93.5(16) 15.7(68) 54.5(22) 23.2(58) 61.2(20) freeman1 7.4(59) 7.9(53) 112.5(24) 9.7(41) 9.8(37) 94.6(23) 7.4(49) 103.6(28) 159.9(22) coke 10.5(65) 36.7(35) 12.7(60) 148.7(4) 25.9(45) 18.7(55) 62.1(17) 20.1(53) 30.8(42) jogging-1 4.3(80) 6.2(62) 112(19) 3.9(81) 144.6(20) 87.9(19) 7.2(75) 113(18) 101.7(19) Average 8.5(75) 69.4(51) 38.2(55) 26.3(61) 61.3(47) 34.7(52) 54.5(40) 51.5(45) 59.8(41)
Table 3. Tracking precision values on 11 different attributes
View tableTable 3. Tracking precision values on 11 different attributes
Algorithm SV (28) IV (25) OCC (29) BC (21) DEF (19) MB (12) FM (17) IPR (31) OPR (39) OV (6) LR (4) Proposed 0.744 0.757 0.792 0.756 0.831 0.624 0.630 0.779 0.791 0.648 0.516 CNT 0.662 0.566 0.662 0.646 0.687 0.507 0.500 0.661 0.672 0.502 0.557 DSST 0.740 0.741 0.725 0.691 0.657 0.603 0.562 0.780 0.732 0.533 0.534 CST 0.707 0.676 0.726 0.773 0.756 0.591 0.516 0.708 0.742 0.596 0.454 SST 0.688 0.603 0.588 0.644 0.487 0.408 0.425 0.630 0.599 0.406 0.527 KCF 0.680 0.729 0.749 0.752 0.741 0.650 0.602 0.725 0.730 0.649 0.379 NRMLC 0.597 0.437 0.583 0.497 0.541 0.378 0.397 0.511 0.546 0.492 0.542 DLT 0.590 0.534 0.574 0.495 0.563 0.453 0.446 0.548 0.561 0.444 0.396 CN 0.554 0.532 0.582 0.642 0.523 0.396 0.416 0.615 0.605 0.434 0.405
Table 4. Tracking success rates on 11 attributes
View tableTable 4. Tracking success rates on 11 attributes
Algorithm SV (28) IV (25) OCC (29) BC (21) DEF (19) MB (12) FM (17) IPR (31) OPR (39) OV (6) LR (4) Proposed 0.541 0.560 0.579 0.551 0.609 0.493 0.491 0.566 0.572 0.537 0.382 CNT 0.508 0.456 0.503 0.488 0.524 0.417 0.404 0.495 0.501 0.439 0.437 DSST 0.451 0.506 0.480 0.492 0.474 0.458 0.433 0.532 0.491 0.490 0.352 CST 0.466 0.486 0.506 0.567 0.551 0.474 0.411 0.496 0.514 0.509 0.349 SST 0.504 0.459 0.436 0.489 0.391 0.313 0.340 0.451 0.437 0.347 0.407 KCF 0.427 0.494 0.513 0.533 0.533 0.499 0.461 0.497 0.496 0.550 0.310 NRMLC 0.427 0.341 0.437 0.370 0.392 0.303 0.334 0.367 0.389 0.410 0.428 DLT 0.455 0.405 0.423 0.339 0.394 0.363 0.360 0.411 0.412 0.367 0.346 CN 0.363 0.390 0.404 0.453 0.388 0.329 0.334 0.437 0.418 0.410 0.311
Table 5. Tracking speed comparison of 9 trackers
View tableTable 5. Tracking speed comparison of 9 trackers
Tracker Proposed CNT DSST CST SST KCF NRMLC DLT CN Code M M M M M+C M M M M Platform CPU CPU+GPU CPU CPU CPU CPU+GPU CPU CPU CPU+GPU CPU Tracking speed /(frame·s-1) 43.2 56.3 5 24 2.2 2.2 172 1.28 15 - Note: M, MATLAB; C, C++
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Xiufeng Liao, Zhiqiang Hou, Wangsheng Yu, Jiaoyao Wang, Chuanhua Chen. A Scale Adapted Tracking Algorithm Based on Kernelized Correlation[J]. Acta Optica Sinica, 2018, 38(7): 0715002
Paper Information
Category: Machine Vision
Received: Nov. 8, 2017
Accepted: --
Published Online: Sep. 5, 2018
The Author Email: Hou Zhiqiang (zhq@sohu.com)
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