Object Tracking using End-to-End Detection and Deep Association Metric

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dc.contributor.author Yasmeen, Arowa
dc.contributor.author Rahman, Fariha Ishrat
dc.contributor.author Hassan, Inara Zahin
dc.date.accessioned 2022-04-17T17:16:08Z
dc.date.available 2022-04-17T17:16:08Z
dc.date.issued 2021-03-30
dc.identifier.uri http://hdl.handle.net/123456789/1349
dc.description Supervised by Prof. Dr. Kamrul Hasan Co Supervisor, Mr. Hasan Mahmud, Department of Computer Science and Engineering(CSE), Islamic University of Technology(IUT), Board Bazar, Gazipur-1704, Bangladesh. en_US
dc.description.abstract Object Tracking has multiple major applications such as video surveillance for se- curity, traffic control, contact tracing, human computer interaction, gesture recog- nition, augmented reality, video editing, robotics etc. Often, to perform real-time tracking, video surveillance applications forgo detection accuracy in favour of de- tection speed. This paper proposes a combination of object detection and object tracking algorithms that gives an improvement on both detection accuracy and speed compared to existing video surveillance solutions. It also includes a method to trace the movement of a target from video surveillance footage and visualise the target’s path on a 2D map en_US
dc.language.iso en en_US
dc.title Object Tracking using End-to-End Detection and Deep Association Metric en_US
dc.type Thesis en_US


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