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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Leoputra, W.S. Venkatesh, S. Tan, T. |
| Copyright Year | 2008 |
| Description | Author affiliation: Curtin Univ. of Technol., Perth, WA (Leoputra, W.S.; Venkatesh, S.; Tan, T.) |
| Abstract | In this paper, we present a novel person detection system for public transport buses tackling the problem of changing illumination conditions. Our approach integrates a stable SIFT (scale invariant feature transform) background seat modeling mechanism with a human shape model into a weighted Bayesian framework to detect passengers on-board buses. SIFT background modeling extracts local stable features on the pre-annotated background seat areas and tracks these features over time to build a global statistical background model for each seat. Since SIFT features are partially invariant to lighting, this background model can be used robustly to detect the seat occupancy status even under severe lighting changes. The human shape model further confirms the existence of a passenger when a seat is occupied. This constructs a robust passenger monitoring system which is resilient to illumination changes. We evaluate the performance of our proposed system on a number of challenging video datasets obtained from bus cameras and the experimental results show that it is superior to state-of-art people detection systems. |
| Starting Page | 719 |
| Ending Page | 725 |
| File Size | 1524099 |
| Page Count | 7 |
| File Format | |
| ISBN | 9781424422869 |
| DOI | 10.1109/ICARCV.2008.4795606 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2008-12-17 |
| Publisher Place | Vietnam |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Monitoring Humans Robotics and automation Australia Lighting Shape Bayesian methods Robustness Cameras Road vehicles Homography Bayesian Inference SIFT Background Model Elliptical Human Detection |
| Content Type | Text |
| Resource Type | Article |
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