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2012 | OriginalPaper | Chapter

Simulation Videos for Understanding Occlusion Effects on Kernel Based Object Tracking

Authors : Beng Yong Lee, Lee Hung Liew, Wai Shiang Cheah, Yin Chai Wang

Published in: Computer Science and its Applications

Publisher: Springer Netherlands

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Abstract

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 introduced 64 simulation video sequences to experiment the effectiveness of each tracking methods on various occlusion scenarios. Tracking performances are evaluated based on Sequence Frame Detection Accuracy (SFDA). The results showed that Mean shift tracker would fail completely when full occlusion occurred. Kalman filter tracker achieved highest SFDA score of 0.85 when tracking object with uniform trajectory and no occlusion. Results also demonstrated that Particle filter tracker fails to detect object with non-uniform trajectory. The effect of occlusion on each tracker is analyzed with Frame Detection Accuracy (FDA) graph.

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Metadata
Title
Simulation Videos for Understanding Occlusion Effects on Kernel Based Object Tracking
Authors
Beng Yong Lee
Lee Hung Liew
Wai Shiang Cheah
Yin Chai Wang
Copyright Year
2012
Publisher
Springer Netherlands
Book
Computer Science and its Applications

Print ISBN: 978-94-007-5698-4

Electronic ISBN: 978-94-007-5699-1

Copyright Year: 2012

DOI
https://doi.org/10.1007/978-94-007-5699-1_15

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