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...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2012
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| Subjects: | |
| Online Access: | http://ir.unimas.my/id/eprint/17380/1/Measuring%20the%20Effects%20of%20Occlusion%20on%20Kernel%20Based%20Object%20Tracking%20%28abstract%29.pdf http://ir.unimas.my/id/eprint/17380/ http://www.sciencedirect.com/science/article/pii/S1877705812026410 https://doi.org/10.1016/j.proeng.2012.07.241 |
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| Summary: | 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. |
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