Multi-Target Vehicle Tracking Algorithm Based on Improved DeepSORT
In this paper, we address the issues of insufficient accuracy and frequent identity switching in the multi-target tracking algorithm DeepSORT by proposing two improvement strategies. First, we optimize the appearance feature extraction process by training a lightweight appearance extraction network...
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| Format: | Article |
| Language: | English |
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MDPI AG
2024-10-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/24/21/7014 |
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| author | Dudu Guo Zhuzhou Li Hongbo Shuai Fei Zhou |
| author_facet | Dudu Guo Zhuzhou Li Hongbo Shuai Fei Zhou |
| author_sort | Dudu Guo |
| collection | DOAJ |
| description | In this paper, we address the issues of insufficient accuracy and frequent identity switching in the multi-target tracking algorithm DeepSORT by proposing two improvement strategies. First, we optimize the appearance feature extraction process by training a lightweight appearance extraction network (OSNet) on a vehicle re-identification dataset. This makes the appearance features better suited for the vehicle tracking model required in our paper. Second, we improve the metric of motion features by using the original IOU distance metric or GIOU metrics. The optimized tracking algorithm using GIOU achieves effective improvements in tracking precision and accuracy. The experimental results show that the improved vehicle tracking models MOTA and IDF1 are enhanced by 4.6% and 5.9%, respectively. This allows for the stable tracking of vehicles and reduces the occurrence of identity switching phenomenon to a certain extent. |
| format | Article |
| id | doaj-art-df5f649afb494d808033df0b6b14c9b8 |
| institution | Kabale University |
| issn | 1424-8220 |
| language | English |
| publishDate | 2024-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj-art-df5f649afb494d808033df0b6b14c9b82024-11-08T14:41:52ZengMDPI AGSensors1424-82202024-10-012421701410.3390/s24217014Multi-Target Vehicle Tracking Algorithm Based on Improved DeepSORTDudu Guo0Zhuzhou Li1Hongbo Shuai2Fei Zhou3The School of Transportation Engineering, Xinjiang University, Urumqi 830017, ChinaThe School of Intelligent Manufacturing for Modern Industry, Xinjiang University, Urumqi 830017, ChinaThe School of Intelligent Manufacturing for Modern Industry, Xinjiang University, Urumqi 830017, ChinaThe School of Intelligent Manufacturing for Modern Industry, Xinjiang University, Urumqi 830017, ChinaIn this paper, we address the issues of insufficient accuracy and frequent identity switching in the multi-target tracking algorithm DeepSORT by proposing two improvement strategies. First, we optimize the appearance feature extraction process by training a lightweight appearance extraction network (OSNet) on a vehicle re-identification dataset. This makes the appearance features better suited for the vehicle tracking model required in our paper. Second, we improve the metric of motion features by using the original IOU distance metric or GIOU metrics. The optimized tracking algorithm using GIOU achieves effective improvements in tracking precision and accuracy. The experimental results show that the improved vehicle tracking models MOTA and IDF1 are enhanced by 4.6% and 5.9%, respectively. This allows for the stable tracking of vehicles and reduces the occurrence of identity switching phenomenon to a certain extent.https://www.mdpi.com/1424-8220/24/21/7014optical satellite imagesvehicle detectionYOLOXscale expansionattention mechanismBFPnet |
| spellingShingle | Dudu Guo Zhuzhou Li Hongbo Shuai Fei Zhou Multi-Target Vehicle Tracking Algorithm Based on Improved DeepSORT Sensors optical satellite images vehicle detection YOLOX scale expansion attention mechanism BFPnet |
| title | Multi-Target Vehicle Tracking Algorithm Based on Improved DeepSORT |
| title_full | Multi-Target Vehicle Tracking Algorithm Based on Improved DeepSORT |
| title_fullStr | Multi-Target Vehicle Tracking Algorithm Based on Improved DeepSORT |
| title_full_unstemmed | Multi-Target Vehicle Tracking Algorithm Based on Improved DeepSORT |
| title_short | Multi-Target Vehicle Tracking Algorithm Based on Improved DeepSORT |
| title_sort | multi target vehicle tracking algorithm based on improved deepsort |
| topic | optical satellite images vehicle detection YOLOX scale expansion attention mechanism BFPnet |
| url | https://www.mdpi.com/1424-8220/24/21/7014 |
| work_keys_str_mv | AT duduguo multitargetvehicletrackingalgorithmbasedonimproveddeepsort AT zhuzhouli multitargetvehicletrackingalgorithmbasedonimproveddeepsort AT hongboshuai multitargetvehicletrackingalgorithmbasedonimproveddeepsort AT feizhou multitargetvehicletrackingalgorithmbasedonimproveddeepsort |