Measuring the Effects of Occlusion on Kernel Based Object Tracking Using Simulated Videos

Occlusion handling is one of the most studied problems for object tracking in computer vision. Many previous works claimed that occlusion can be handled effectively using Kalman filter, Particle filter and Mean Shift tracking methods. However, these methods were only tested on specific task videos...

Full description

Bibliographic Details
Main Authors: Beng, Yong Lee, Lee, Hung Liew, WaiShiang, Cheah, Yin, Chai Wang
Format: Article
Language:English
Published: Elsevier 2012
Subjects:
Online Access:http://ir.unimas.my/id/eprint/17380/
http://ir.unimas.my/id/eprint/17380/1/Measuring%20the%20Effects%20of%20Occlusion%20on%20Kernel%20Based%20Object%20Tracking%20%28abstract%29.pdf
_version_ 1848838278842679296
author Beng, Yong Lee
Lee, Hung Liew
WaiShiang, Cheah
Yin, Chai Wang
author_facet Beng, Yong Lee
Lee, Hung Liew
WaiShiang, Cheah
Yin, Chai Wang
author_sort Beng, Yong Lee
building UNIMAS Institutional Repository
collection Online Access
description Occlusion handling is one of the most studied problems for object tracking in computer vision. Many previous works claimed that occlusion can be handled effectively using Kalman filter, Particle filter and Mean Shift tracking methods. However, these methods were only tested on specific task videos. In order to explore the actual potential of these methods, this paper examined the tracking methods with six simulation videos that consider various occlusion scenarios. Tracking performances are evaluated based on Sequence Frame Detection Accuracy (SFDA). The results show that Mean shift tracker would fail completely when full occlusion occurs as claimed by many previous works. In most cases, Kalman filter and Particle filter tracker achieved SFDA score between 0.3 and 0.4. It demonstrates that Particle filter tracker fails to detect object with arbitrary movement in one of the experiments. The effect of occlusion on each tracker is analysed with Frame Detection Accuracy (FDA) graph.
first_indexed 2025-11-15T06:53:01Z
format Article
id unimas-17380
institution Universiti Malaysia Sarawak
institution_category Local University
language English
last_indexed 2025-11-15T06:53:01Z
publishDate 2012
publisher Elsevier
recordtype eprints
repository_type Digital Repository
spelling unimas-173802022-09-29T08:04:13Z http://ir.unimas.my/id/eprint/17380/ Measuring the Effects of Occlusion on Kernel Based Object Tracking Using Simulated Videos Beng, Yong Lee Lee, Hung Liew WaiShiang, Cheah Yin, Chai Wang T Technology (General) Occlusion handling is one of the most studied problems for object tracking in computer vision. Many previous works claimed that occlusion can be handled effectively using Kalman filter, Particle filter and Mean Shift tracking methods. However, these methods were only tested on specific task videos. In order to explore the actual potential of these methods, this paper examined the tracking methods with six simulation videos that consider various occlusion scenarios. Tracking performances are evaluated based on Sequence Frame Detection Accuracy (SFDA). The results show that Mean shift tracker would fail completely when full occlusion occurs as claimed by many previous works. In most cases, Kalman filter and Particle filter tracker achieved SFDA score between 0.3 and 0.4. It demonstrates that Particle filter tracker fails to detect object with arbitrary movement in one of the experiments. The effect of occlusion on each tracker is analysed with Frame Detection Accuracy (FDA) graph. Elsevier 2012 Article PeerReviewed text en http://ir.unimas.my/id/eprint/17380/1/Measuring%20the%20Effects%20of%20Occlusion%20on%20Kernel%20Based%20Object%20Tracking%20%28abstract%29.pdf Beng, Yong Lee and Lee, Hung Liew and WaiShiang, Cheah and Yin, Chai Wang (2012) Measuring the Effects of Occlusion on Kernel Based Object Tracking Using Simulated Videos. Procedia Engineering, 41. pp. 764-770. ISSN 1877-7058 http://www.sciencedirect.com/science/article/pii/S1877705812026410 https://doi.org/10.1016/j.proeng.2012.07.241
spellingShingle T Technology (General)
Beng, Yong Lee
Lee, Hung Liew
WaiShiang, Cheah
Yin, Chai Wang
Measuring the Effects of Occlusion on Kernel Based Object Tracking Using Simulated Videos
title Measuring the Effects of Occlusion on Kernel Based Object Tracking Using Simulated Videos
title_full Measuring the Effects of Occlusion on Kernel Based Object Tracking Using Simulated Videos
title_fullStr Measuring the Effects of Occlusion on Kernel Based Object Tracking Using Simulated Videos
title_full_unstemmed Measuring the Effects of Occlusion on Kernel Based Object Tracking Using Simulated Videos
title_short Measuring the Effects of Occlusion on Kernel Based Object Tracking Using Simulated Videos
title_sort measuring the effects of occlusion on kernel based object tracking using simulated videos
topic T Technology (General)
url http://ir.unimas.my/id/eprint/17380/
http://ir.unimas.my/id/eprint/17380/
http://ir.unimas.my/id/eprint/17380/
http://ir.unimas.my/id/eprint/17380/1/Measuring%20the%20Effects%20of%20Occlusion%20on%20Kernel%20Based%20Object%20Tracking%20%28abstract%29.pdf