Three-Dimensional Multi-Object Tracking Based on Feature Fusion and Similarity Estimation Network

Wenming Chen; Ru Hong; Shaoyan Gai; Feipeng Da

doi:10.3788/AOS202242.1615001

Acta Optica Sinica, Volume. 42, Issue 16, 1615001(2022)

Three-Dimensional Multi-Object Tracking Based on Feature Fusion and Similarity Estimation Network

Wenming Chen^1,2, Ru Hong^1,2, Shaoyan Gai^1,2、*, and Feipeng Da^1,2,3、**

Author Affiliations

¹School of Automation, Southeast University, Nanjing 210096, Jiangsu , China

²Key Laboratory of Measurement and Control of Complex Systems of Engineering, Ministry of Education, Southeast University, Nanjing 210096, Jiangsu , China

³Shenzhen Research Institute, Southeast University, Shenzhen 518063, Guangdong , China

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Abstract Get PDF(in Chinese)

Figures & Tables(15)

Fig. 1. Flow chart of proposed algorithm. (a) Object detection; (b) feature extraction; (c) feature fusion; (d) similarity estimation; (e) data association

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Fig. 2. 2D feature extraction network model

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Fig. 3. 3D feature extraction network model

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Fig. 4. SE module

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Fig. 5. Feature fusion module

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Fig. 6. Structure of similarity estimation network

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Fig. 7. Flow chart of greedy matching

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Fig. 8. Method B in single modal network

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Fig. 9. Different methods in multimodal networks. (a) Method C; (b) method D; (c) method E

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Fig. 10. HOTA of different modalities and different fusion methods on KITTI verification set

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Fig. 11. [in Chinese]

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Fig. 12. Bird's eye view of tracking result comparison on verification set. (a) Proposed algorithm; (b) comparison algorithm 1; (c) comparison algorithm 2; (d) comparison algorithm 3; (e) comparison algorithm 4; (f) comparison algorithm 5

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Table 1. Accuracy of different modalities and different fusion methods on KITTI verification set
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Table 1. Accuracy of different modalities and different fusion methods on KITTI verification set
Modality Method HOTA /% M_AssA /%
Image A 62.02 57.00
B 62.21 57.26
Point A 70.84 74.15
B 70.97 74.41
Fusion-add C 71.20 74.83
D 71.39 75.28
E 71.54 75.55
Fusion-cat C 71.27 74.98
D 71.43 75.36
E 71.66 75.79

Table 2. Accuracy of different similarity estimation methods on KITTI verification set
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Table 2. Accuracy of different similarity estimation methods on KITTI verification set
Method Threshold HOTA /% M_AssA /%
Euclidean distance 0.5 54.84 44.91
5.0 55.41 46.11
50.0 54.85 44.63
Cosine similarity 0.5 52.73 41.70
0.0 54.26 44.15
-0.5 52.42 41.23
Similarity estimation network 0.5 71.66 75.79

Table 3. Accuracies of different algorithms on KITTI test set

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Table 3. Accuracies of different algorithms on KITTI test set

Algorithm	HOTA /%	M_AssA /%	M_DetA /%
Complexer-YOLO	49.12	39.34	62.44
CIWT	54.90	49.99	60.57
Point3DT	57.20	59.15	55.71
mmMOT	62.05	54.02	72.29
Quasi-Dense	68.45	65.49	72.44
SRK_ODESA	68.51	65.49	75.40
MOTFusion	68.74	66.16	72.19
Proposed	69.24	68.46	70.71

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Wenming Chen, Ru Hong, Shaoyan Gai, Feipeng Da. Three-Dimensional Multi-Object Tracking Based on Feature Fusion and Similarity Estimation Network[J]. Acta Optica Sinica, 2022, 42(16): 1615001

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Paper Information

Category: Machine Vision

Received: Jan. 13, 2022

Accepted: Mar. 29, 2022

Published Online: Aug. 4, 2022

The Author Email: Gai Shaoyan (qxxymm@163.com), Da Feipeng (dafp@seu.edu.cn)

DOI:10.3788/AOS202242.1615001

Topics

laser devices and laser physics

Lasers and Laser Optics

Laser physics

laser manufacturing

Instrumentation, Measurement and Metrology

Table 1. Accuracy of different modalities and different fusion methods on KITTI verification set

Table 1. Accuracy of different modalities and different fusion methods on KITTI verification set

Table 2. Accuracy of different similarity estimation methods on KITTI verification set

Table 2. Accuracy of different similarity estimation methods on KITTI verification set

Table 3. Accuracies of different algorithms on KITTI test set

Table 3. Accuracies of different algorithms on KITTI test set