Object Tracking Algorithm Based on Global Feature Matching Processing of Laser Point Cloud

Qishu Qian; Yihua Hu; Nanxiang Zhao; Minle Li; Fucai Shao

doi:10.3788/LOP57.061012

Laser & Optoelectronics Progress, Volume. 57, Issue 6, 061012(2020)

Object Tracking Algorithm Based on Global Feature Matching Processing of Laser Point Cloud

Qishu Qian^1,2, Yihua Hu^1,2、*, Nanxiang Zhao^1,2, Minle Li^1,2, and Fucai Shao³

¹State Key Laboratory of Pulsed Power Laser Technology, College of Electronic Engineering, National University of Defense Technology, Hefei, Anhui 230037, China

²Anhui Provincial Key Laboratory of Electronic Restriction, Hefei, Anhui 230037, China

³Military Representative Bureau of the Ministry of Equipment Development of the Central Military Commission in Beijing, Beijing 100191, China

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Figures & Tables(11)

Fig. 1. Point cloud target recognition process based on SVR selection

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Fig. 2. SVR values of six objects for two LIDAR-object distances

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Fig. 3. Point cloud target tracking flow based on global feature matching

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Fig. 4. Visualization of different datasets. (a) Dataset 1; (b) dataset 2

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Fig. 5. Object tracking results in the N th frame. (a)(g) N=40; (b)(h) N=80; (c)(i) N=120; (d)(j) N=160; (e)(k) N=200; (f)(l) N=240

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Fig. 6. Execution time of each part in object tracking based on different datasets. (a) Dataset 1; (b) dataset 2

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Table 1. Comparison of four global feature descriptors

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Table 1. Comparison of four global feature descriptors

Descriptor	Histogram length	Information	Pre-procession	Normalization
VFH	308	Angle	Normal	Yes
CVFH	308	Angle	Normal, segmentation	None
GRSD	21	Distance	Normal, voxelization, surface categorization	None
ESF	640	Angle, distance, area	None	Yes

Table 2. Parameters of the scene simulation
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Table 2. Parameters of the scene simulation
Target Size /(m×m×m) Target speed /(m·s^-1) Platform speed /(m·s^-1) Pitch /(°) Yaw /(°)
0 340
Jeep 3.83×1.68×1.51 20 0 15-60 0-180
20 340

Table 3. Recognition rate comparison of four feature descriptors%
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Table 3. Recognition rate comparison of four feature descriptors%
Descriptor LIDAR-target range /m
300 600 900 1200 1500 1800
VFH 76.7 68.2 53.0 55.5 41.0 40.5
CVFH 89.3 90.0 91.5 83.5 74.0 47.8
GRSD 49.6 44.2 35.4 22.2 26.6 13.2
ESF 99.0 99.0 94.0 76.5 71.0 54.7

Table 4. Recognition rate comparison with and without SVR selection

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Table 4. Recognition rate comparison with and without SVR selection

Parameter		VFH	CVFH	GRSD	ESF
	Recognition rate without SVR selection /%	55.8	79.4	31.9	82.4
Dataset 1	Recognition rate with SVR selection /%	59.9	82.6	35.5	84.9
	Increased recognition rate /%	4.1	3.2	3.6	2.5
	Execution time /ms	3.6	4.5	31.0	39.0
	Recognition rate without SVR selection /%	57.5	80.1	34.3	84.0
Dataset 2	Recognition rate with SVR selection /%	62.3	83.3	38.6	86.7
	Increased recognition rate /%	4.8	3.2	4.3	3.7
	Execution time /ms	6.2	7.8	109.0	110.0

Table 5. Tracking accuracy of sight line in the Nth frame

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Table 5. Tracking accuracy of sight line in the Nth frame

Parameter		N
Parameter			40						80	120	160	200	240
Dataset 1	Accuracy without SVR selection /%	50.0		71.3	72.7	74.1	75.3	80.3
	Accuracy with SVR selection /%	55.1		75.1	79.9	80.4	83.2	87.7
Dataset 2	Accuracy without SVR selection /%	81.3		82.0	80.5	80.3	81.7	82.5
	Accuracy with SVR selection /%	86.3		86.7	83.3	85.0	84.3	86.0

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Qishu Qian, Yihua Hu, Nanxiang Zhao, Minle Li, Fucai Shao. Object Tracking Algorithm Based on Global Feature Matching Processing of Laser Point Cloud[J]. Laser & Optoelectronics Progress, 2020, 57(6): 061012

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