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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Feris, R. Brown, L.M. Pankanti, S. Ming-Ting Sun |
| Copyright Year | 2014 |
| Description | Author affiliation: Univ. of Washington, Seattle, WA, USA (Ming-Ting Sun) || IBM T. J. Watson Res. Center, Yorktown Heights, NY, USA (Feris, R.; Brown, L.M.; Pankanti, S.) |
| Abstract | Practical surveillance systems deployed in urban scenarios need to operate 24/7 under a wide range of environmental conditions. As modern video analytics shift from blob-based to object-centered architectures, appearance-based object detection under different weather conditions and lighting effects emerges as a critical yet largely unaddressed problem. This paper investigates this research topic, using as a case study the problem of vehicle detection in urban surveillance environments. In particular, we show that a simple and efficient Winsorized lighting correction technique improves performance significantly when outliers due to shadows, specularities, headlights, and occluders are present. Moreover, we demonstrate that a self-training mechanism utilizing a balanced training set automatically acquired from the target domain yields superior performance. Our experimental results are carried out on a novel dataset of vehicle images collected from a public traffic camera and categorized according to multiple environmental conditions. |
| Starting Page | 166 |
| Ending Page | 171 |
| File Size | 2006610 |
| Page Count | 6 |
| File Format | |
| ISBN | 9781479952090 |
| ISSN | 10514651 |
| DOI | 10.1109/ICPR.2014.38 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2014-08-24 |
| Publisher Place | Sweden |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Detectors Lighting Cameras Vehicles Surveillance Training Standards |
| Content Type | Text |
| Resource Type | Article |
| Subject | Computer Vision and Pattern Recognition |
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