Acta Optica Sinica, Volume. 40, Issue 4, 0415002(2020)
Siamese Neural Network Object Tracking with Distractor-Aware Model
Figures & Tables(13)
Fig. 4. Precision plots and success plots of the eight trackers. (a) Success rate; (b) accuracy
Fig. 5. Actual results of eight algorithms. (a) Faceocc1; (b) subway; (c) football; (d) freeman1; (e) dog1;(f) carScale; (g) mountainBike; (h) david2; (i) faceocc2; (j) basketball
Fig. 6. Success rate and accuracy of various tracking algorithms in aerial video sequences. (a) Accuracy; (b) success rate
Table 1. Ten sets of video attributes
View tableTable 1. Ten sets of video attributes
Video sequence Length / frame Resolution ratio / (pixel×pixel) Characteristic David2 537 320×240 In-plane rotation, out-of-plane rotation Faceocc1 892 352×288 Occlusion Faceocc2 812 320×240 Illumination variation, occlusion, in-plane rotation, out-of-plane rotation Subway 175 352×288 Occlusion, deformation, background clutter Freeman1 326 360×240 Scale variation, in-plane rotation, out-of-plane rotation MountainBike 228 640×360 Out-of-plane rotation, in-plane rotation, background clutter Dog1 1350 320×240 Scale variation, in-plane rotation, out-of-plane rotation CarScale 252 640×272 Scale variation, occlusion, fast motion, in-plane rotation, out-of-plane rotation Football 362 624×352 Occlusion, in-plane rotation, out-of-plane rotation, background clutter Basketball 725 576×432 Illumination variation, out-of-plane rotation, occlusion, deformation, background clutter
Table 2. Tracking errors of tracking algorithms in ten video sequences
View tableTable 2. Tracking errors of tracking algorithms in ten video sequences
Sequence Our Siamfc DSiamM ASLA TLD MEEM MUSTER IVT MountainBike 5.6199 6.1406 5.7915 8.9727 213.3278 13.0037 8.12 7.416 Faceocc1 10.1931 11.9656 11.4831 77.8108 27.3678 16.9904 14.2932 17.8346 Freeman1 5.9435 6.6078 6.039 104.8774 39.6988 11.3029 8.6361 11.7283 Subway 2.4955 3.254 2.9104 137.6901 159.0114 4.1169 2.2211 130.2318 Football 5.2967 6.7392 5.0698 15.3724 14.2587 5.1423 14.7789 14.8367 CarScale 15.7498 15.318 18.4334 24.9002 50.3495 67.2993 18.6758 11.7225 Basketball 10.4241 22.7174 10.658 82.6266 268.7569 4.2104 4.8487 106.9015 Faceocc2 10.1346 10.7052 10.1493 19.5059 12.2779 10.5872 5.8895 7.1397 Dog1 5.0088 3.004 3.5086 5.8068 4.1903 6.1053 4.0696 4.0764 David2 3.7716 2.8061 3.007 1.5874 4.9788 1.863 1.9849 1.6066
Table 3. Ten sets of aerial video sequence attributes
View tableTable 3. Ten sets of aerial video sequence attributes
Video sequence Length / frame Resolution ratio / (pixel×pixel) Characteristic Wakeboard4 233 1280×720 Scale variation, aspect ratio change, viewpoint change Wakeboard10 157 1280×720 Scale variation, low resolution Boat1 301 1280×720 Scale variation Boat2 267 1280×720 Scale variation Boat6 269 1280×720 Scale variation Boat9 467 1280×720 Scale variation, aspect ratio change, low resolution, partial occlusion, viewpoint change Building1 157 1280×720 Truck3 179 1280×720 Low resolution, partial occlusion, background clutter Car4 449 1280×720 Occlusion, aspect ratio change, low resolution, partial occlusion, camera motion, similar object Car5 249 1280×720 Scale variation
Table 4. Accuracy of tracking algorithms in ten video sequences
View tableTable 4. Accuracy of tracking algorithms in ten video sequences
Sequence Our MUSTER DSiamM ASLA Siamfc TLD MEEM IVT Wakeboard4 0.751 0.549 0.588 0.004 0.305 0.077 0.597 0.004 Wakeboard10 1.000 1.000 1.000 0.917 1.000 1.000 1.000 0.248 Boat1 1.000 0.841 1.000 0.990 1.000 0.498 0.658 0.957 Boat2 1.000 1.000 1.000 1.000 1.000 0.397 1.000 1.000 Boat6 0.933 0.892 0.914 0.955 0.922 0.885 0.818 0.981 Boat9 0.953 0.914 0.522 0.829 0.972 0.473 0.469 0.203 Building1 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 Truck3 1.000 1.000 1.000 1.000 0.207 1.000 1.000 1.000 Car4 0.989 0.998 0.998 0.457 0.296 0.998 0.296 0.450 Car5 1.000 0.908 1.000 1.000 1.000 0.996 0.265 0.743
Table 5. Success rate of tracking algorithms in ten video sequences
View tableTable 5. Success rate of tracking algorithms in ten video sequences
Sequence Our MUSTER DSiamM ASLA Siamfc TLD MEEM IVT Wakeboard4 0.434 0.312 0.363 0.009 0.185 0.029 0.348 0.010 Wakeboard10 0.567 0.396 0.631 0.365 0.552 0.437 0.333 0.146 Boat1 0.730 0.731 0.722 0.529 0.740 0.595 0.376 0.612 Boat2 0.748 0.745 0.754 0.773 0.745 0.624 0.618 0.817 Boat6 0.786 0.339 0.774 0.602 0.763 0.346 0.329 0.618 Boat9 0.526 0.332 0.325 0.273 0.534 0.201 0.071 0.116 Building1 0.816 0.803 0.830 0.764 0.742 0.737 0.781 0.793 Truck3 0.613 0.794 0.702 0.832 0.132 0.753 0.694 0.787 Car4 0.756 0.635 0.706 0.362 0.227 0.785 0.244 0.369 Car5 0.757 0.721 0.766 0.521 0.722 0.729 0.412 0.526
Table 6. Average speed comparison of the algorithms
View tableTable 6. Average speed comparison of the algorithms
Algorithm Our Siamfc MUSTER DSiamM ASLA TLD MEEM IVT Speed /(frame·s-1) 37.2 56.1 3.7 23.9 8.1 27.8 9.9 39.9
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Yong Li, Dedong Yang, Yajun Han, Peng Song. Siamese Neural Network Object Tracking with Distractor-Aware Model[J]. Acta Optica Sinica, 2020, 40(4): 0415002
Paper Information
Category: Machine Vision
Received: Aug. 26, 2019
Accepted: Nov. 6, 2019
Published Online: Feb. 11, 2020
The Author Email: Yang Dedong (ydd12677@163.com)
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