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End-to-End Two-Frame Online Multiple Object Tracking Using Convolutional Neural Networks

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dc.contributor.advisor Dailey, Prof. Matthew N.
dc.contributor.author Suwannaphum, Chaipat
dc.contributor.other Autariya, Dr. Chutiporn
dc.contributor.other Ekpanyapong, Dr. Mongkol
dc.date.accessioned 2020-05-13T13:36:14Z
dc.date.available 2020-05-13T13:36:14Z
dc.date.issued 2020-05
dc.identifier.uri http://www.cs.ait.ac.th/xmlui/handle/123456789/972
dc.description.abstract In this thesis, I propose a framework based on a convolutional neural network to perform multiple object tracking. In particular, I extend the architecture of the convolutional neural network used in YOLOv3, an object detection algorithm, to perform short (twoframe) tracking. The proposed network takes two image frames as input, detects objects in one frame, and outputs the locations of the objects in the other frame. Short tracks are combined in a post processing step to generate long tracks. The network tracks multiple objects simultaneously using only a single forward pass of two image frames. This makes the tracking framework more efficient compared to methods based on neural networks that follow a traditional tracking-by-detection strategy, which requires repeated comparison of two sets of detections to score similarities when performing data association. Experimental results on real world data, a quantitaive evaluation, and comparison with other methods are also included. en_US
dc.description.sponsorship His Majesty the King’s Scholarships (Thailand) en_US
dc.publisher AIT en_US
dc.subject Object detection, Online multiple object tracking, End-to-end deep learn- ing. en_US
dc.title End-to-End Two-Frame Online Multiple Object Tracking Using Convolutional Neural Networks en_US
dc.type Thesis en_US


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