Infrared and Laser Engineering, Volume. 54, Issue 2, 20240373(2025)
Sparse multiple hypothesis matching and model lightweighting for infrared multi-object tracking
Fig. 3. A set of cost matrices is obtained by grouping based on pseudo-depth information
Fig. 5. The question of the trajectory trees is converted to the question of maximum clique
Fig. 6. (a) The CBS module is not split; (b) The CBS module is divided into two parts
Fig. 9. (a) Impact of threshold
Fig. 11. (a) Images with small objects; (b) Feature maps of small objects before pruning in YOLOv8s; (c) Feature maps of small objects after pruning in YOLOv8s; (d) Images with large objects; (e) Feature maps of large objects before pruning in YOLOv8s; (f) Feature maps of large objects after pruning in YOLOv8s
Fig. 12. Comparison of the number of channels before and after pruning
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Changqi XU, Haoxian WANG, Jun WANG, Zhiquan ZHOU. Sparse multiple hypothesis matching and model lightweighting for infrared multi-object tracking[J]. Infrared and Laser Engineering, 2025, 54(2): 20240373
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Received: Nov. 13, 2024
Accepted: --
Published Online: Mar. 14, 2025
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