Acta Optica Sinica, Volume. 42, Issue 16, 1615001(2022)
Three-Dimensional Multi-Object Tracking Based on Feature Fusion and Similarity Estimation Network
The multi-sensor information fusion method of the existing multi-object tracking algorithms for self-driving cannot give full play to synergy. To solve this problem, a three-dimensional multi-object tracking algorithm based on multi-modal feature fusion and learnable object similarity estimation is proposed. The multi-modal feature fusion module fuses the feature of images and point clouds on the basis of the channel attention mechanism to further improve the expressive ability of multi-modal features. The object similarity estimation module directly generates the similarity matrix through the network, and realizes the cross-modal joint reasoning between multiple objects in a learnable way, which avoids massive manual parameter setting. The proposed algorithm is verified and tested on the KITTI data set, and its higher-order tracking accuracy (HOTA) reaches 69.24% in the test set, which indicates that the algorithm is superior to other algorithms in accuracy and has good robustness.
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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
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)