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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Bansal, M. Matei, B. Sawhney, H. Sang-Hack Jung Eledath, J. |
| Copyright Year | 2009 |
| Description | Author affiliation: Sarnoff Corporation, Princeton, NJ, USA (Bansal, M.; Matei, B.; Sawhney, H.; Sang-Hack Jung; Eledath, J.) |
| Abstract | We propose a principled statistical approach for using 3D information and scene context to reduce the number of false positives in stereo based pedestrian detection. Current pedestrian detection algorithms have focused on improving the discriminability of 2D features that capture the pedestrian appearance, and on using various classifier architectures. However, there has been less focus on exploiting the geometry and spatial context in the scene to improve pedestrian detection performance. We make several contributions: (i) we define a new 3D feature, called a Vertical Support Histogram, from dense stereo range maps to locally characterize 3D structure; (ii) we estimate the likelihoods of these 3D features using kernel density estimation, and use them within a Markov Random Field (MRF) to enforce spatial constraints between the features, and to obtain the Maximum A-Posteriori (MAP) scene labeling; (iii) we employ the MAP scene labelings to reduce the number of candidate windows that are tested by a standard, state-of-the-art pedestrian appearance classifier. We evaluate our algorithm on a very challenging, publicly available stereo dataset and compare the performance with state-of-the-art methods. |
| Starting Page | 31 |
| Ending Page | 38 |
| File Size | 1860114 |
| Page Count | 8 |
| File Format | |
| ISBN | 9781424444427 |
| DOI | 10.1109/ICCVW.2009.5457720 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2009-09-27 |
| Publisher Place | Japan |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Geometry Maximum a posteriori estimation Histograms Layout Labeling State estimation Detection algorithms Kernel Markov random fields Testing |
| Content Type | Text |
| Resource Type | Article |
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