Acta Optica Sinica, Volume. 39, Issue 5, 0515002(2019)
Multi-Scale Context-Aware Correlation Filter Tracking Algorithm Based on Channel Reliability
Figures & Tables(10)
Fig. 2. Illustration of channel reliability. (a) Response map of 6th frame; (b) 6th frame; (c) 108th frame; (d) response map of 108th frame
Fig. 3. Curves of distance precision and success rate of eight algorithms. (a) Distance precision curves of overall; (b) Success rate curves of overall
Fig. 4. Distance precision and success rate curves for different attribute scenes. (a) Illumination variation; (b) scale variation; (c) occlusion; (d) rotation variation
Fig. 5. Tracking results using different approaches. (a) Lemming; (b) Liquor; (c) Shaking; (d) Singer2; (e) Soccer; (f) Terris
Table 1. Characteristics of testing tracking algorithms
View tableTable 1. Characteristics of testing tracking algorithms
No. Tracker Feature Scale adaption Context-aware Channel reliability 1 CSK Raw pixel ○ ○ ○ 2 KCF_MTSA HOG √ ○ ○ 3 SAMF Raw pixel+HOG+CN √ ○ ○ 4 MOSSE_CA Raw pixel ○ √ ○ 5 DCF_CA HOG ○ √ ○ 6 SAMF_CA Raw pixel+HOG+CN √ √ ○ 7 RPT HOG √ ○ ○ 8 Proposed method Raw pixel+HOG+CN √ √ √ Note: √ means yes, ○ means no.
Table 2. Characteristics of image sequences in the experiment
View tableTable 2. Characteristics of image sequences in the experiment
Sequence Image size /(pixel×pixel) Target size /(pixel×pixel) Characteristic Lemming 640×480 61×103 IV, SV, OCC, FM, OPR, OV Liquor 640×480 73×210 IV, SV, OCC, MB, FM, OPR, OV, BC Shaking 624×352 61×71 IV, SV, IPR, OPR, BC Singer2 624×352 67×122 IV, DEF, IPR, OPR, BC Soccer 640×360 67×81 IV, SV, OCC, MB, FM, IPR, OPR, BC Terris 320×240 68×101 IV, SV, IPR, OPR, BC
Table 3. Comparison of average CLEpixel
View tableTable 3. Comparison of average CLEpixel
Sequence Proposed CSK MOSSE_CA DCF_CA SAMF KCF_MTSA SAMF_CA RPT Lemming 9.048 114.687 95.529 152.994 15.136 13.452 11.258 42.699 Liquor 7.409 158.997 95.547 19.996 10.578 8.641 9.253 38.641 Shaking 8.651 14.813 73.448 90.76 44.502 48.466 10.628 8.978 Singer2 9.919 185.826 176.144 11.402 13.238 11.124 13.256 10.837 Soccer 19.502 63.940 41.057 23.758 17.304 100.238 28.729 10.826 Terris 4.309 26.533 72.579 7.635 11.661 13.081 7.421 4.881
Table 4. OP of different algorithms
View tableTable 4. OP of different algorithms
Sequence Proposed CSK MOSSE_CA DCF_CA SAMF KCF_MTSA SAMF_CA RPT Lemming 1.000 0.462 0.540 0.299 0.739 0.842 0.915 0.635 Liquor 1.000 0.289 0.211 0.871 0.839 0.879 0.895 0.871 Shaking 1.000 0.523 0.222 0.802 0.745 0.723 0.904 0.921 Singer2 1.000 0.063 0.060 0.753 0.826 0.912 0.902 0.926 Soccer 0.931 0.263 0.556 0.895 0.895 0.247 0.881 0.989 Terris 1.000 0.612 0.681 0.973 0.918 1.000 0.856 1.000
Table 5. Tracking speeds of different trackersframe·s-1
View tableTable 5. Tracking speeds of different trackersframe·s-1
Sequence Proposed MOSSE_CA CSK DCF_CA KCF_MTSA SAMF SAMF_CA RPT Lemming 12.156 89.766 181.341 53.520 22.165 14.091 13.756 1.036 Liquor 19.256 203.726 454.252 89.831 43.680 23.492 20.101 2.258 Shaking 14.941 121.417 234.255 66.328 27.799 17.272 16.445 1.267 Singer2 9.668 80.358 154.209 25.617 16.076 12.856 11.360 0.895 Soccer 25.253 115.114 289.206 60.062 29.231 31.097 26.772 3.653 Terris 16.964 84.671 172.258 48.411 23.354 20.742 18.013 1.518
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Mingfeng Yin, Yuming Bo, Jianliang Zhu, Panlong Wu. Multi-Scale Context-Aware Correlation Filter Tracking Algorithm Based on Channel Reliability[J]. Acta Optica Sinica, 2019, 39(5): 0515002
Paper Information
Category: Machine Vision
Received: Jan. 5, 2019
Accepted: Jan. 22, 2019
Published Online: May. 10, 2019
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