Object Scale Adaptation Tracking Based on Full-Convolutional Siamese Networks

Fig. 5. Comparison of tracking results of 10 algorithm. (a) Jumping (MB、FM); (b) CarScale (SV、OCC、FM、IPR、OPR); (c) Couple (SV、DEF、FM、BC、OPR); (d) Soccer (IV、SV、OCC、MB、FM、IPR、OPR、BC); (e) Ironman (IV、SV、OCC、MB、FM、IPR、OPR、OV、BC、LR); (f) Girl (SV、OCC、IPR、OPR); (g)Skiing (IV、SV、DEF、IPR、OPR); (h) Liquor (IV、SV、OCC、FM、IPR、OPR、OV、BC); (i) Woman (IV、SV、OCC、DEF、MB、FM、OPR)

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Fig. 6. Precision and success rate of 50 sequences. (a) Precision; (b) success rate

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Fig. 7. Precision and success rate on FM and SV attributes. (a) Precision on FM attribute; (b) success rate on FM attribute; (c) precision plot on SV attribute; (d) success rate on SV attribute

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Fig. 8. Tracking precision and success rate on (a)(b) IV, (c)(d) OCC, (e)(f) LR, (g)(h) DEF, (i)(j) BC, and (k)(l) OV attributes

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Table 1. Architecture of CNN of proposed algorithm

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Table 1. Architecture of CNN of proposed algorithm

Layer	Support	Chan.map	Stride	For exemplar	For search	Chans
				127×127	255×127	×3
Conv1	11×11	96×3	2	59×59	123×127	×96
Pool1	3×3		2	29×29	61×61	×96
Conv2	5×5	256×48	1	25×25	57×57	×256
Pool2	3×3		2	12×12	28×28	×256
Conv3	3×3	384×256	1	10×10	26×26	×192
Conv4	3×3	384×192	1	8×8	24×24	×192
Conv5	3×3	256×192	1	6×6	22×22	×168

Table 2. Tracking speed comparison of proposed algorithm and other algorithms
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Table 2. Tracking speed comparison of proposed algorithm and other algorithms
Tracker Proposed CF2 DCFNet Siamfc HDT
Platform GPU GPU GPU GPU GPU
FPS /(frame·s^-1) 42.3 11 65.94 58 10
Real Y N Y Y N

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Xiaoxia Sun, Chunjiang Pang. Object Scale Adaptation Tracking Based on Full-Convolutional Siamese Networks[J]. Laser & Optoelectronics Progress, 2019, 56(1): 011502

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