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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Main Author: Ji, Dongsheng
Other Authors: Yap Kim Hui
Format: Thesis-Master by Coursework
Language:English
Published: Nanyang Technological University 2023
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Online Access:https://hdl.handle.net/10356/172688
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Institution: Nanyang Technological University
Language: English
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spelling 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
institution Nanyang Technological University
building NTU Library
continent Asia
country Singapore
Singapore
content_provider NTU Library
collection DR-NTU
language English
topic Engineering::Electrical and electronic engineering
spellingShingle Engineering::Electrical and electronic engineering
Ji, Dongsheng
Multiple object tracking using artificial intelligence
description 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.
author2 Yap Kim Hui
author_facet Yap Kim Hui
Ji, Dongsheng
format Thesis-Master by Coursework
author Ji, Dongsheng
author_sort 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
title_fullStr Multiple object tracking using artificial intelligence
title_full_unstemmed Multiple object tracking using artificial intelligence
title_sort multiple object tracking using artificial intelligence
publisher Nanyang Technological University
publishDate 2023
url https://hdl.handle.net/10356/172688
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