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

Journals >Laser & Optoelectronics Progress >Volume 57 >Issue 6 >Page 061012 > Article

Laser & Optoelectronics Progress
Vol. 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

Author Affiliations

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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DOI: 10.3788/LOP57.061012 Cite this Article Set citation alerts

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 Copy Citation Text

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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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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 1. Comparison of four global feature descriptors

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 2. Parameters of the scene simulation

Descriptor	LIDAR-target range /m
Descriptor	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 3. Recognition rate comparison of four feature descriptors%

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 4. Recognition rate comparison with and without SVR selection

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

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

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