Journal of Computer Applications ›› 2023, Vol. 43 ›› Issue (5): 1372-1377.DOI: 10.11772/j.issn.1001-9081.2022030377
Special Issue: 第九届中国数据挖掘会议(CCDM 2022)
• China Conference on Data Mining 2022 (CCDM 2022) • Previous Articles Next Articles
Guangyao ZHANG1,2, Chunfeng SONG1,2()
Received:
2022-03-28
Revised:
2022-05-11
Accepted:
2022-05-19
Online:
2023-05-08
Published:
2023-05-10
Contact:
Chunfeng SONG
About author:
ZHANG Guangyao, born in 1996, M. S. candidate. His research interests include object detection, multi-object tracking.Supported by:
通讯作者:
宋纯锋
作者简介:
张广耀(1996—),男,山东省济南人,硕士研究生,主要研究方向:目标检测、多目标跟踪基金资助:
CLC Number:
Guangyao ZHANG, Chunfeng SONG. Pedestrian head tracking model based on full-body appearance features[J]. Journal of Computer Applications, 2023, 43(5): 1372-1377.
张广耀, 宋纯锋. 融合人体全身表观特征的行人头部跟踪模型[J]. 《计算机应用》唯一官方网站, 2023, 43(5): 1372-1377.
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URL: https://www.joca.cn/EN/10.11772/j.issn.1001-9081.2022030377
跟踪模型 | MOTA/% | IDF1/% | FP | FN | IDs |
---|---|---|---|---|---|
固定比例全身框 | 60.0 | 65.6 | 172 040 | 299 111 | 4 349 |
头框表观特征 | 62.7 | 66.3 | 156 074 | 282 454 | 4 231 |
不使用姿态 | 62.7 | 66.5 | 156 669 | 282 232 | 4 250 |
HT-FF | 62.7 | 66.6 | 156 512 | 282 308 | 4 198 |
Tab. 1 Ablation experimental results
跟踪模型 | MOTA/% | IDF1/% | FP | FN | IDs |
---|---|---|---|---|---|
固定比例全身框 | 60.0 | 65.6 | 172 040 | 299 111 | 4 349 |
头框表观特征 | 62.7 | 66.3 | 156 074 | 282 454 | 4 231 |
不使用姿态 | 62.7 | 66.5 | 156 669 | 282 232 | 4 250 |
HT-FF | 62.7 | 66.6 | 156 512 | 282 308 | 4 198 |
全身框生成形式 | mAP | AP50 | AP75 |
---|---|---|---|
固定比例生成 | 25.2 | 72.1 | 9.7 |
动态生成 | 42.3 | 88.2 | 34.5 |
Tab. 2 Precision comparison between fixed ratio full-body bounding box generation and adaptive full-body bounding box generation unit: %
全身框生成形式 | mAP | AP50 | AP75 |
---|---|---|---|
固定比例生成 | 25.2 | 72.1 | 9.7 |
动态生成 | 42.3 | 88.2 | 34.5 |
跟踪模型 | MOTA/% | IDF1/% | FP | FN | IDs |
---|---|---|---|---|---|
HeadHunter-T[ | 57.8 | 53.9 | 51 840 | 299 459 | 4 394 |
FairMOT[ | 60.8 | 62.8 | 118 109 | 198 896 | 12 781 |
HT-FF | 64.0 | 63.2 | 124 794 | 172 432 | 5 771 |
Tab. 3 Results comparison of different models on Head Tracking 21 test set
跟踪模型 | MOTA/% | IDF1/% | FP | FN | IDs |
---|---|---|---|---|---|
HeadHunter-T[ | 57.8 | 53.9 | 51 840 | 299 459 | 4 394 |
FairMOT[ | 60.8 | 62.8 | 118 109 | 198 896 | 12 781 |
HT-FF | 64.0 | 63.2 | 124 794 | 172 432 | 5 771 |
跟踪模型 | MOTA/% | IDF1/% | FP | FN | IDs |
---|---|---|---|---|---|
HeadHunter-T[ | 43.2 | 34.7 | 254 154 | 361 787 | 70 244 |
SORT[ | 52.9 | 46.2 | 100 705 | 373 318 | 11 221 |
HT-FF | 62.7 | 66.6 | 156 512 | 282 308 | 4 198 |
Tab. 4 Tracking result based on same detection results
跟踪模型 | MOTA/% | IDF1/% | FP | FN | IDs |
---|---|---|---|---|---|
HeadHunter-T[ | 43.2 | 34.7 | 254 154 | 361 787 | 70 244 |
SORT[ | 52.9 | 46.2 | 100 705 | 373 318 | 11 221 |
HT-FF | 62.7 | 66.6 | 156 512 | 282 308 | 4 198 |
1 | WOJKE N, BEWLEY A, PAULUS D. Simple online and realtime tracking with a deep association metric[C]// Proceedings of the 2017 IEEE International Conference on Image Processing. Piscataway: IEEE, 2017: 3645-3649. 10.1109/icip.2017.8296962 |
2 | SUNDARARAMAN R, DE ALMEIDA BRAGA C, MARCHAND E, et al. Tracking pedestrian heads in dense crowd[C]// Proceedings of the 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Piscataway: IEEE, 2021: 3864-3874. 10.1109/cvpr46437.2021.00386 |
3 | SONG Y M, YOON K, YOON Y C, et al. Online multi-object tracking with GMPHD filter and occlusion group management[J]. IEEE Access, 2019, 7: 165103-165121. 10.1109/access.2019.2953276 |
4 | BRASÓ G, LEAL-TAIXÉ L. Learning a neural solver for multiple object tracking[C]// Proceedings of the 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Piscataway: IEEE, 2020: 6246-6256. 10.1109/cvpr42600.2020.00628 |
5 | MIAO J X, WU Y, LIU P, et al. Pose-guided feature alignment for occluded person re-identification[C]// Proceedings of the 2019 IEEE/CVF International Conference on Computer Vision. Piscataway: IEEE, 2019: 542-551. 10.1109/iccv.2019.00063 |
6 | GIRSHICK R, DONAHUE J, DARRELL T, et al. Rich feature hierarchies for accurate object detection and semantic segmentation[C]// Proceedings of the 2014 IEEE Conference on Computer Vision and Pattern Recognition. Piscataway: IEEE, 2014: 580-587. 10.1109/cvpr.2014.81 |
7 | BERNARDIN K, STIEFELHAGEN R. Evaluating multiple object tracking performance: the CLEAR MOT metrics[J]. EURASIP Journal on Image and Video Processing, 2008, 2008: No.246309. 10.1155/2008/246309 |
8 | RISTANI E, SOLERA F, ZOU R, et al. Performance measures and a data set for multi-target, multi-camera tracking[C]// Proceedings of the 2016 European Conference on Computer Vision, LNCS 9914. Cham: Springer, 2016: 17-35. |
9 | FRITSCH J, KÜHNL T, GEIGER A. A new performance measure and evaluation benchmark for road detection algorithms[C]// Proceedings of the 16th International IEEE Conference on Intelligent Transportation Systems. Piscataway: IEEE, 2013: 1693-1700. 10.1109/itsc.2013.6728473 |
10 | ETTINGER S, CHENG S Y, CAINE B, et al. Large scale interactive motion forecasting for autonomous driving: the Waymo open motion dataset[C]// Proceedings of the 2021 IEEE/CVF International Conference on Computer Vision. Piscataway: IEEE, 2021: 9690-9699. 10.1109/iccv48922.2021.00957 |
11 | CIAPARRONE G, LUQUE SÁNCHEZ F, TABIK S, et al. Deep learning in video multi-object tracking: a survey[J]. Neurocomputing, 2020, 381: 61-88. 10.1016/j.neucom.2019.11.023 |
12 | KALMAN R E. A new approach to linear filtering and prediction problems[J]. Journal of Basic Engineering, 1960, 82(1): 35-45. 10.1115/1.3662552 |
13 | 俞皓芳,孙力帆,付主木. 基于改进K-means++聚类的多扩展目标跟踪算法[J]. 计算机应用, 2020, 40(1):271-277. |
YU H F, SUN L F, FU Z M. Multi-extended target tracking algorithm based on improved K-means++ clustering[J]. Journal of Computer Applications, 2020, 40(1):271-277. | |
14 | 黄凯文,凌六一,王成军,等. 基于改进YOLO和DeepSORT的实时多目标跟踪[J]. 电子测量技术, 2022, 45(6):7-13. |
HUANG K W, LING L Y, WANG C J, et al. Real-time multiple object tracking algorithm based on improved YOLO and DeepSORT[J]. Electronic Measurement Technology, 2022, 45(6):7-13. | |
15 | 曲优,李文辉. 基于多任务联合学习的多目标跟踪方法[J/OL]. 吉林大学学报(工学版) (2022-03-07) [2022-04-20].. |
QU Y, LI W H. Multiple object tracking method based on multi-task joint learning[J/OL]. Journal of Jilin University (Engineering and Technology Edition) (2022-03-07) [2022-04-20].. | |
16 | WANG Z D, ZHENG L, LIU Y X, et al. Towards real-time multi-object tracking[C]// Proceedings of the 2020 European Conference on Computer Vision, LNCS 12356. Cham: Springer, 2020: 107-122. |
17 | ZHANG Y F, WANG C Y, WANG X G, et al. FairMOT: on the fairness of detection and re-identification in multiple object tracking[J]. International Journal of Computer Vision, 2021, 129(11): 3069-3087. 10.1007/s11263-021-01513-4 |
18 | HE J W, HUANG Z H, WANG N Y, et al. Learnable graph matching: incorporating graph partitioning with deep feature learning for multiple object tracking[C]// Proceedings of the 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Piscataway: IEEE, 2021: 5295-5305. 10.1109/cvpr46437.2021.00526 |
19 | DENDORFER P, REZATOFIGHI H, MILAN A, et al. MOT20: a benchmark for multi object tracking in crowded scenes[EB/OL]. [2022-02-03].. |
20 | REN S Q, HE K M, GIRSHICK R, et al. Faster R-CNN: towards real-time object detection with region proposal networks [C]// Proceedings of the 28th International Conference on Neural Information Processing Systems — Volume 1. Cambridge: MIT Press, 2015: 91-99. |
21 | LIN T Y, GOYAL P, GIRSHICK R, et al. Focal loss for dense object detection[C]// Proceedings of the 2017 IEEE International Conference on Computer Vision. Piscataway: IEEE, 2017: 2999-3007. 10.1109/iccv.2017.324 |
22 | ZHOU X Y, WANG D Q, KRÄHENBÜHL P. Objects as points[EB/OL]. (2019-04-25) [2022-02-03].. 10.5260/chara.21.2.8 |
23 | YU F, WANG D Q, SHELHAMER E, et al. Deep layer aggregation[C]// Proceedings of the 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Piscataway: IEEE, 2018: 2403-2412. 10.1109/cvpr.2018.00255 |
24 | CHEN Y, TIAN Y, HE M. Monocular human pose estimation: a survey of deep learning-based methods[J]. Computer Vision and Image Understanding, 2020, 192: No.102897. 10.1016/j.cviu.2019.102897 |
25 | FANG H S, XIE S Q, TAI Y W, et al. RMPE: regional multi-person pose estimation[C]// Proceedings of the 2017 IEEE International Conference on Computer Vision. Piscataway: IEEE, 2017: 2353-2362. 10.1109/iccv.2017.256 |
26 | HE K M, ZHANG X Y, REN S Q, et al. Deep residual learning for image recognition[C]// Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition. Piscataway: IEEE, 2016: 770-778. 10.1109/cvpr.2016.90 |
27 | SHAO S, ZHAO Z J, LI B X, et al. CrowdHuman: a benchmark for detecting human in a crowd[EB/OL]. [2022-02-03].. |
28 | KINGMA D P, BA J L. Adam: a method for stochastic optimization[EB/OL]. [2022-03-01].. |
29 | BEWLEY A, GE Z Y, OTT L, et al. Simple online and realtime tracking[C]// Proceedings of the 2016 IEEE International Conference on Image Processing. Piscataway: IEEE, 2016: 3464-3468. 10.1109/icip.2016.7533003 |
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