| Autor: |
Maglogiannis, Ilias, Karpouzis, Kostas, Bramer, Max, Miao, Gengxin, Luo, Yupin, Tian, Qiming, Tang, Jingxin |
| Zdroj: |
Artificial Intelligence Applications & Innovations (9780387342238); 2006, p212-220, 9p |
| Abstrakt: |
Most pedestrian detection systems are built based on computer vision technology and usually are composed of two basic modules: object detection module, and recognition module. This paper presents an efficient filtering module, which works between the two basic modules, based on extracting the 3-dimensional information from single frame images. The filter module removes the noisy objects extracted by object detection module and thus reduces the burden of the recognition module. 3-D information, such as height, width and distance are extracted from single frame images. Using this information, a Bayesian classifier is employed to implement the filter. The main contribution of this filter module is that it removed about 30% noisy objects detected by the object detection module. The total computing cost and error detection rate is reduced when this filter module is used in the pedestrian detection system. [ABSTRACT FROM AUTHOR] |
| Databáze: |
Supplemental Index |
| Externí odkaz: |
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