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An Automated Real-Time People Tracking System Based on KLT Features Detection

2010
Journal from gdlhub / 2017-08-14 11:52:31
Oleh : Nijad Al-Najdawi, Sara Tedmori, Eran Edirisinghe, Helmut Bez, IAJIT
Dibuat : 2012-06-22, dengan 1 file

Keyword : Object tracking, kalman-filter, features selection, KLT.
Subjek : An Automated Real-Time People Tracking System Based on KLT Features Detection
Url : http://www.ccis2k.org/iajit/PDF/vol.9,no.1/2868-13.pdf
Sumber pengambilan dokumen : Internet

The advancement of technology allows video acquisition devices to have a better performance, thereby increasing


the number of applications that can effectively utilize digital video. Compared to still images, video sequences provide more


information about how objects and scenarios change over time. Tracking humans is of interest for a variety of applications


including surveillance, activity monitoring and gate analysis. Many efficient object tracking algorithms have been proposed in


literature, however part of those algorithms are semi-automatic requiring human interference. As for the fully automated


algorithms, most of them are not applicable to real-time applications. This paper presents a low cost automatic object tracking


algorithm suitable for use in real-time video based systems. The novelty of the proposed system is that it uses a simplified


version of the Kanade-Lucas-Tomasi (KLT) technique to detect features of both continuous and discontinuous nature. As


discontinuous feature selection is subject to noise, and would result in non-optimal feature based object tracking, the authors


propose the use of a Kalman filter for the purpose of seeking optimal estimates in tracking. The integrated tracking system is


capable of handling shadows and is based on a dynamic background subtraction strategy that minimises errors and quickly


adapts to scene changes. Experimental results are provided to demonstrate the system’s capability of accurately tracking


objects in real-time applications where scenes are subject to noise particularly resulting from occlusions and sudden


illumination variations.

Deskripsi Alternatif :

The advancement of technology allows video acquisition devices to have a better performance, thereby increasing


the number of applications that can effectively utilize digital video. Compared to still images, video sequences provide more


information about how objects and scenarios change over time. Tracking humans is of interest for a variety of applications


including surveillance, activity monitoring and gate analysis. Many efficient object tracking algorithms have been proposed in


literature, however part of those algorithms are semi-automatic requiring human interference. As for the fully automated


algorithms, most of them are not applicable to real-time applications. This paper presents a low cost automatic object tracking


algorithm suitable for use in real-time video based systems. The novelty of the proposed system is that it uses a simplified


version of the Kanade-Lucas-Tomasi (KLT) technique to detect features of both continuous and discontinuous nature. As


discontinuous feature selection is subject to noise, and would result in non-optimal feature based object tracking, the authors


propose the use of a Kalman filter for the purpose of seeking optimal estimates in tracking. The integrated tracking system is


capable of handling shadows and is based on a dynamic background subtraction strategy that minimises errors and quickly


adapts to scene changes. Experimental results are provided to demonstrate the system’s capability of accurately tracking


objects in real-time applications where scenes are subject to noise particularly resulting from occlusions and sudden


illumination variations.

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