光学学报, 2017, 37 (12): 1215006, 网络出版: 2018-09-06 复制并引用该论文  本文已被引 7 次  被引通知

图 & 表

图 1. 方法框架图

Fig. 1. Framework of the method

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图 2. MDNet结构图

Fig. 2. Structure of MDNet

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图 3. 跟踪算法性能的定性比较。(a) seq1; (b) seq2; (c) seq3; (d) seq4; (e) seq5; (f) seq6; (g) seq7; (h) seq8

Fig. 3. Qualitative comparison of tracking algorithm performance. (a) seq1; (b) seq2; (c) seq3; (d) seq4; (e) seq5; (f) seq6; (g) seq7; (h) seq8

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图 4. 中心位置误差比较。(a) seq1; (b) seq2; (c) seq3; (d) seq4; (e) seq5; (f) seq6; (g) seq7; (h) seq8

Fig. 4. Comparison of center location error. (a) seq1; (b) seq2; (c) seq3; (d) seq4; (e) seq5; (f) seq6; (g) seq7; (h) seq8

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表 1实验中的测试图像序列

Table1. Experimental test image sequences

Image sequence True target Track/Speed Main challenge seq1 1 Straight line/Faster Single pseudo target, moving fast seq2 2 Curve/Fast Single pseudo target, background interference seq3 3 Straight line/Fast Multiple pseudo targets rendezvous, background interference seq4 4 Curve/Fast Multiple pseudo targets rendezvous, background interference seq5 5 Curve/Faster Multiple pseudo targets rendezvous, background interference, moving fast seq6 6 Straight line/Fast Multiple pseudo targets rendezvous, similar noise seq7 7 Straight line/Slow Multiple pseudo targets rendezvous, similar noise seq8 8 Curve/Fast Multiple pseudo targets rendezvous, background interference

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表 2成功率

Table2. Success rate%

Image sequence DLT DLTAR SAMF CNT DSST KCF MDNet Proposed seq1 51.9 32.9 35.6 55.7 55.4 35.6 55.6 100.0 seq2 92.4 71.7 51.5 51.8 49.5 50.5 51.7 99.5 seq3 57.6 28.4 53.5 52.8 53.2 53.5 52.3 95.3 seq4 16.5 7.2 13.9 19.5 13.7 10.5 21.7 96.4 seq5 18.2 8.1 12.7 19.3 12.4 9.8 21.9 95.8 seq6 38.6 16.4 36.4 39.8 36.3 28.7 40.2 98.7 seq7 37.9 16.2 61.2 39.4 35.8 21.1 41.7 99.1 seq8 17.7 8.1 13.2 20.7 11.2 10.3 22.8 96.6 Mean 41.4 23.6 34.8 37.4 33.4 27.5 38.5 97.7

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表 3平均中心位置误差

Table3. Mean center location errorpixel

Image sequence DLT DLTAR SAMF CNT DSST KCF MDNet Proposed seq1 90.1 98.7 215.9 84.8 88.1 228.3 84.7 1.3 seq2 13.6 14.4 184.8 184.8 186.2 184.9 184.8 0.9 seq3 17.4 16.1 70.5 68.7 70.7 71.5 68.4 4.9 seq4 251.4 257.3 275.2 283.4 258.1 273.5 281.9 40.9 seq5 158.2 149.3 262.4 256.3 272.5 272.5 274.6 46.7 seq6 53.7 48.7 72.8 53.4 45.0 46.7 46.7 1.3 seq7 70.3 68.7 20.2 28.0 26.5 26.0 40.8 1.2 seq8 243.5 233.8 305.2 242.2 317.7 238.4 243.9 40.7 Mean 112.3 110.9 175.9 150.2 158.1 167.7 153.2 17.2

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表 4平均覆盖率

Table4. Mean overlap rate%

Image sequence DLT DLTAR SAMF CNT DSST KCF MDNet Proposed seq1 34.97 32.28 19.66 34.81 32.38 20.64 35.48 64.63 seq2 61.84 59.26 21.37 31.89 26.00 21.40 32.80 64.12 seq3 56.64 58.25 33.04 30.26 33.49 33.40 31.56 60.08 seq4 5.89 5.14 5.57 5.64 6.01 6.02 7.72 31.56 seq5 6.69 7.02 5.10 5.49 5.91 6.14 7.88 30.47 seq6 6.59 6.81 7.32 9.18 9.18 9.76 15.49 39.18 seq7 6.47 7.85 16.30 17.17 15.76 16.68 15.76 39.21 seq8 6.52 7.24 5.39 5.59 6.01 6.03 8.28 46.29 Mean 23.20 22.98 14.22 17.50 16.84 15.00 19.37 46.94

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蔺素珍, 郑瑶, 禄晓飞, 曾建潮. 基于多域卷积神经网络与自回归模型的空中小目标自适应跟踪方法[J]. 光学学报, 2017, 37(12): 1215006. Suzhen Lin, Yao Zheng, Xiaofei Lu, Jianchao Zeng. Adaptive Tracking Algorithm for Aerial Small Targets Based on Multi-Domain Convolutional Neural Networks and Autoregression Model[J]. Acta Optica Sinica, 2017, 37(12): 1215006.