Please use this identifier to cite or link to this item:
Full metadata record
DC FieldValueLanguage
dc.contributor.authorGao, Junjieen_US
dc.identifier.citationGao, J. (2022). Deep learning for multiple object tracking. Master's thesis, Nanyang Technological University, Singapore.
dc.description.abstractThe past several years has seen the rapid development of multiple object tracking object detection and re-identification. Most of work focuses on pedestrian body tracking with one-shot anchor-free structure and few work is conducted on face tracking. The main reason is that the pedestrian tracking always conduct on surveillance video with limited resolution. Human faces in these videos are usually not clear enough to distinguish them from each other. As the result, body tracking became an important alternative technology when face recognition fails. This dissertation will focus on the face tracking when the resolution of video is high enough. Human face detector usually apply anchor-based detector to obtain the better performance. However, one-shot anchor-based detector performs badly on re-identification task because of serious network fuzziness. Our face detection network applies the anchor-free structure on face detector and the performance is just slightly worse than the state-of-the-arts anchor-based face detector. Traditional method to train the re-identification branch usually append a full connection layer after the output of extracted id feature to do id classification. My work combines this traditional strategy with the idea of metric learning together to ensure the robustness of trained identity information.en_US
dc.publisherNanyang Technological Universityen_US
dc.subjectEngineering::Electrical and electronic engineeringen_US
dc.titleDeep learning for multiple object trackingen_US
dc.typeThesis-Master by Courseworken_US
dc.contributor.supervisorLin Zhipingen_US
dc.contributor.schoolSchool of Electrical and Electronic Engineeringen_US
dc.description.degreeMaster of Science (Signal Processing)en_US
item.fulltextWith Fulltext-
Appears in Collections:EEE Theses
Files in This Item:
File Description SizeFormat 
  Restricted Access
2.78 MBAdobe PDFView/Open

Page view(s)

Updated on Feb 28, 2024


Updated on Feb 28, 2024

Google ScholarTM


Items in DR-NTU are protected by copyright, with all rights reserved, unless otherwise indicated.