Multiple object tracking using artificial intelligence
Multiple Object Tracking (MOT) is a key focus in machine vision research. It seeks to follow and label several objects in a video over time, calculating their paths. The challenge is to create a model that works well in tough conditions like occlusions and shadows. Different MOT models based on v...
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sg-ntu-dr.10356-1726882023-12-22T15:45:06Z Multiple object tracking using artificial intelligence Ji, Dongsheng Yap Kim Hui School of Electrical and Electronic Engineering EKHYap@ntu.edu.sg Engineering::Electrical and electronic engineering Multiple Object Tracking (MOT) is a key focus in machine vision research. It seeks to follow and label several objects in a video over time, calculating their paths. The challenge is to create a model that works well in tough conditions like occlusions and shadows. Different MOT models based on various principles have been suggested, and while accuracy has improved, there’s still a need to enhance the model’s performance. The dissertation employs BoT-SORT [1] as its baseline. However, BoT-SORT faces challenges in object recognition and re-identification, particularly in sce narios with object overlap, occlusion, and uniform visual attributes [2] [3] [4]. To address this, the dissertation integrates various re-identification (ReID) back bones with the BoT-SORT architecture to assess resulting model performance. Among the three models created, the one with the SBS R50 ReID backbone achieves the highest IDF1 score of 81.9%. To enable visual observation and fa cilitate comparison of tracker performance, tracking samples are executed under diverse video conditions. The dissertation includes a detailed analysis of perfor mance results and a discussion of future research directions for MOT. Keywords: Multiple Object Tracking, BoT-SORT, Re-identification. Master of Science (Communications Engineering) 2023-12-18T06:01:44Z 2023-12-18T06:01:44Z 2023 Thesis-Master by Coursework Ji, D. (2023). Multiple object tracking using artificial intelligence. Master's thesis, Nanyang Technological University, Singapore. https://hdl.handle.net/10356/172688 https://hdl.handle.net/10356/172688 en application/pdf Nanyang Technological University |
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Engineering::Electrical and electronic engineering Ji, Dongsheng Multiple object tracking using artificial intelligence |
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Multiple Object Tracking (MOT) is a key focus in machine vision research. It
seeks to follow and label several objects in a video over time, calculating their
paths. The challenge is to create a model that works well in tough conditions
like occlusions and shadows. Different MOT models based on various principles
have been suggested, and while accuracy has improved, there’s still a need to
enhance the model’s performance.
The dissertation employs BoT-SORT [1] as its baseline. However, BoT-SORT
faces challenges in object recognition and re-identification, particularly in sce narios with object overlap, occlusion, and uniform visual attributes [2] [3] [4].
To address this, the dissertation integrates various re-identification (ReID) back bones with the BoT-SORT architecture to assess resulting model performance.
Among the three models created, the one with the SBS R50 ReID backbone
achieves the highest IDF1 score of 81.9%. To enable visual observation and fa cilitate comparison of tracker performance, tracking samples are executed under
diverse video conditions. The dissertation includes a detailed analysis of perfor mance results and a discussion of future research directions for MOT.
Keywords: Multiple Object Tracking, BoT-SORT, Re-identification. |
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Yap Kim Hui |
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Yap Kim Hui Ji, Dongsheng |
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Thesis-Master by Coursework |
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Ji, Dongsheng |
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Ji, Dongsheng |
title |
Multiple object tracking using artificial intelligence |
title_short |
Multiple object tracking using artificial intelligence |
title_full |
Multiple object tracking using artificial intelligence |
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Multiple object tracking using artificial intelligence |
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Multiple object tracking using artificial intelligence |
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multiple object tracking using artificial intelligence |
publisher |
Nanyang Technological University |
publishDate |
2023 |
url |
https://hdl.handle.net/10356/172688 |
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