Robust object detection and tracking for real-time application

The report is mainly a detailed documentation of the development of the mean shift method application in Kernel based object tracking. The author will study in depth the concepts of the object detection and object tracking and the implementation of the object tracking and object detection algorithm....

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Main Author: Pung, Wei Wei
Other Authors: Teoh Eam Khwang
Format: Final Year Project
Language:English
Published: 2013
Subjects:
Online Access:http://hdl.handle.net/10356/52601
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Institution: Nanyang Technological University
Language: English
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spelling sg-ntu-dr.10356-526012023-07-07T16:25:07Z Robust object detection and tracking for real-time application Pung, Wei Wei Teoh Eam Khwang School of Electrical and Electronic Engineering DRNTU::Engineering::Electrical and electronic engineering The report is mainly a detailed documentation of the development of the mean shift method application in Kernel based object tracking. The author will study in depth the concepts of the object detection and object tracking and the implementation of the object tracking and object detection algorithm. The fundamental concepts and background of the mean shift method in object tracking also will be studied in depth. Then the computation of mean shift method in object tracking will be implemented in Matlab. The implementation will started with the basic computation of mean shift tracking method. Next, the author will modify the basic mean shift tracking method. The orientation based approach will be introduced. The tracking results will be discussed and compared. We will obtain the results that below our expectation with the average dropping of 33.75%. The further improvement and modification will be done to get the better tracking results. The modification is done by the implementation of four boxes tracking window algorithm. The improvement of 42.33% of the average performance is obtained after the modification. . In order to better improve the tracking results, the implementation of fusion system is introduced in the last session of the report. The fusion system consists of object tracking algorithm and object detection algorithm. The average tracking results is further improved by 43.69% by fusion system. Finally the recommendation for further research of the project is discussed. Bachelor of Engineering 2013-05-21T02:20:32Z 2013-05-21T02:20:32Z 2013 2013 Final Year Project (FYP) http://hdl.handle.net/10356/52601 en Nanyang Technological University 108 p. application/pdf
institution Nanyang Technological University
building NTU Library
continent Asia
country Singapore
Singapore
content_provider NTU Library
collection DR-NTU
language English
topic DRNTU::Engineering::Electrical and electronic engineering
spellingShingle DRNTU::Engineering::Electrical and electronic engineering
Pung, Wei Wei
Robust object detection and tracking for real-time application
description The report is mainly a detailed documentation of the development of the mean shift method application in Kernel based object tracking. The author will study in depth the concepts of the object detection and object tracking and the implementation of the object tracking and object detection algorithm. The fundamental concepts and background of the mean shift method in object tracking also will be studied in depth. Then the computation of mean shift method in object tracking will be implemented in Matlab. The implementation will started with the basic computation of mean shift tracking method. Next, the author will modify the basic mean shift tracking method. The orientation based approach will be introduced. The tracking results will be discussed and compared. We will obtain the results that below our expectation with the average dropping of 33.75%. The further improvement and modification will be done to get the better tracking results. The modification is done by the implementation of four boxes tracking window algorithm. The improvement of 42.33% of the average performance is obtained after the modification. . In order to better improve the tracking results, the implementation of fusion system is introduced in the last session of the report. The fusion system consists of object tracking algorithm and object detection algorithm. The average tracking results is further improved by 43.69% by fusion system. Finally the recommendation for further research of the project is discussed.
author2 Teoh Eam Khwang
author_facet Teoh Eam Khwang
Pung, Wei Wei
format Final Year Project
author Pung, Wei Wei
author_sort Pung, Wei Wei
title Robust object detection and tracking for real-time application
title_short Robust object detection and tracking for real-time application
title_full Robust object detection and tracking for real-time application
title_fullStr Robust object detection and tracking for real-time application
title_full_unstemmed Robust object detection and tracking for real-time application
title_sort robust object detection and tracking for real-time application
publishDate 2013
url http://hdl.handle.net/10356/52601
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