Opto-Electronic Engineering, Volume. 50, Issue 12, 230218-1(2024)
Improved CSTrack algorithm for multi-class ship multi-object tracking
Figures & Tables(14)
Fig. 1. Flowchart of the JDE and CSTrack algorithms. (a) JDE; (b) CSTrack
Fig. 2. Network architecture of the CSTrack. (a) Overall framework; (b) CCN and Res_CCN networks; (c) SAAN network; (d) SAM network; (e) CAM network
Fig. 3. Overall framework and feature extraction network architecture of the proposed method
Fig. 8. Comparison of visualization results between our method and baseline on SMD validation set. (a) FN and FP; (b) ID switch and FN
Fig. 9. Comparison of visualization results between our method and baseline on MOT validation set. (a) FP and FN; (b) ID switch and special FP
Table 1. Adjusted SMD dataset video sequence related parameters
View tableView in ArticleTable 1. Adjusted SMD dataset video sequence related parameters
SMD视频序列 视频帧数 Ferry Vessel-ship Speed-boat Boat Kayak Sail-boat 调整前 调整后 MVI_1448 600 - 3210 1410 - - - 测试集 - MVI_1474 445 890 3560 - - - - 测试集 - MVI_1484 600 600 1200 - - - - 测试集 - MVI_1486 600 1023 4200 - - - - 测试集 - MVI_1582 540 540 5400 - - - - 测试集 - MVI_1612 261 165 2349 - - - - 测试集 - MVI_1626 556 - 2775 - - - - 测试集 - MVI_1627 600 - 4200 - - - - 测试集 - MVI_1640 310 - 1677 274 - - - 测试集 - MVI_0797 600 - 767 - - - - 测试集 - MVI_1587 600 - 7800 - 600 - - 训练集 测试集 MVI_1592 491 491 2347 - - 791 - 训练集 测试集 MVI_1452 340 - 1360 - - - 340 训练集 测试集 MVI_1469 600 - 3600 941 - - - 验证集 训练集 MVI_1578 505 - 3535 - - - - 验证集 训练集 MVI_0790 600 - 70 - 140 - - 验证集 训练集 MVI_0799 600 - 390 170 - - - - 训练集
Table 2. Influence of different modules on the tracking performance on MOT16 dataset
View tableView in ArticleTable 2. Influence of different modules on the tracking performance on MOT16 dataset
模型 MOTA↑ IDF1↑ FP↓ FN↓ MT↑ ML↓ IDS↓ Baseline 79.4 77.9 6235 15584 354 29 876 Baseline+Res2net* 82.8 79.7 4714 13966 390 21 616 Baseline+CA 82.4 78.3 4776 14022 377 21 642 Baseline+检测头解耦 82.7 79.2 4628 14318 375 28 571 Baseline* 82.2 75.4 4927 13801 389 23 875 Baseline*+Res2net 83.2 75.8 4459 13350 398 22 758 Baseline*+Res2net* 83.1 80.8 4413 13720 385 23 536 Baseline*+ Res2net* +CA注意力机制(Baseline*+RES_CCN) 83.4 81.9 4335 13434 393 18 571 Baseline*+ Res2net* +CA注意力机制+检测头解耦 84.0 81.3 4000 13107 400 20 480
Table 3. Influence of different attention mechanisms on tracking performance
View tableView in ArticleTable 3. Influence of different attention mechanisms on tracking performance
模型 MOTA↑ IDF1↑ FP↓ FN↓ MT↑ ML↓ IDS↓ SE 83.0 78.6 4624 13557 394 18 589 CBAM 83.6 80.8 4229 13402 391 20 491 ECA 80.5 79.3 3316 17806 351 29 489 CA 84.0 81.3 4000 13107 400 20 480
Table 4. Influence of ReID weight parameters on tracking performance
View tableView in ArticleTable 4. Influence of ReID weight parameters on tracking performance
ReID权重参数 MOTA↑ IDF1↑ FP↓ FN↓ MT↑ ML↓ IDS↓ 4X10-2 80.1 83.0 4685 14488 374 28 576 4X10-3 81.1 82.6 4416 13687 388 23 530 4X10-4 84.0 81.3 4000 13107 400 20 480 4X10-5 83.5 80.2 4319 13379 396 22 530
Table 5. Comparison of tracking performance between the proposed method and other state-of-the-art methods on SMD dataset
View tableView in ArticleTable 5. Comparison of tracking performance between the proposed method and other state-of-the-art methods on SMD dataset
算法 MOTA↑ IDF1↑ FP↓ FN↓ MT↑ ML↓ IDS↓ DeepSORT 31.1 62.3 21678 11082 69 25 224 StrongSORT 42.1 65 13264 17233 63 21 224 ByteTrack 44.8 67.3 9387 17003 57 26 49 CSTrack 38.5 62.6 9760 19617 48 33 109 本文方法 46.9 65.7 6658 16565 43 23 172
Tools
Get Citation
Copy Citation Text
Zhian Yuan, Yu Gu, Gan Ma. Improved CSTrack algorithm for multi-class ship multi-object tracking[J]. Opto-Electronic Engineering, 2024, 50(12): 230218-1
Paper Information
Category: Research Articles
Received: Sep. 1, 2023
Accepted: Dec. 5, 2023
Published Online: Mar. 26, 2024
The Author Email: Gu Yu (谷雨)
© Copyright 2018-2021 | Chinese Laser Press.
All Rights Reserved 沪ICP备15018463号-20