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Scene Flow based Hand Tracking using Deep Learning

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dc.contributor.author Anwer, Muhammad Adnan
dc.date.accessioned 2023-12-19T06:44:40Z
dc.date.available 2023-12-19T06:44:40Z
dc.date.issued 2023
dc.identifier.other 363845
dc.identifier.uri http://10.250.8.41:8080/xmlui/handle/123456789/41293
dc.description Supervisor: Dr. Muhammad Jameel Nawaz Malik en_US
dc.description.abstract Tracking of human hand is an integral part of many key computer vision systems with diverse applications such as human computer interaction, gesture recognition and augmented reality etc. However this task is very challenging due to the non rigid nature of hands, heavy occlusions due to interaction and it’s smaller size relative to the camera frame. Sceneflow estimation is also one of the key algorithms used in many applications such as image processing, navigation and visual surveillance etc. This task is also very difficult due to the one-to-many correspondence prob lem.In this project, we will develop a deep learning based system for estimation of sceneflow and extend it to solve the hand tracking problem. en_US
dc.language.iso en en_US
dc.publisher School of Electrical Engineering and Computer Sciences (SEECS), NUST en_US
dc.title Scene Flow based Hand Tracking using Deep Learning en_US
dc.type Thesis en_US


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