Knowledge Commons of Institute of Automation,CAS
A cascaded classifier for pedestrian detection | |
Xu, Y. W.; Cao, X. B.; Qiao, H.; Wang, F. Y. | |
2006 | |
会议名称 | IEEE Intelligent Vehicles Symposium |
会议录名称 | 2006 IEEE INTELLIGENT VEHICLES SYMPOSIUM |
会议日期 | JUN 13-15, 2006 |
会议地点 | Meguroku, JAPAN |
摘要 |
In a pedestrian defection system, the most critical requirement is to quickly and reliably determine whether a candidate region contains a pedestrian. It is essential to design an effective classifier for pedestrian defection. Until now, most of the existing pedestrian detection systems only adopt a single and non-cascaded classifier However, since the scene is complex and the candidate regions are too many (in our experiments, there are more than 40,000 candidate regions); it is difficult to make the recognition both accurate and fast with such a non-cascaded classifier.
In this paper, we present a cascaded classifier for pedestrian detection. The cascaded classifier combines a statistical learning classifier and a support vector machine classifier. The statistical learning classifier is used to select preliminary candidates, and then the Support vector machine classifier is applied to do a further acknowledgement. This kind of cascaded architecture can take both advantages of the two classifiers, so the detecting rate and defecting speed can be balanced Experimental results illustrate that the cascaded classifier is effective for a real-time detection. |
关键词 | Image Classification / Learning (Artificial Intelligence / Object Detection / Support Vector Machines / Traffic Engineering Computing / Cascaded Classifier / Pedestrian Detection / Statistical Learning Classifier / Support Vector Machine Classifier / Cameras |
文献类型 | 会议论文 |
条目标识符 | http://ir.ia.ac.cn/handle/173211/12857 |
专题 | 多模态人工智能系统全国重点实验室_机器人理论与应用 |
通讯作者 | Xu, Y. W. |
作者单位 | Univ Sci & Technol China, Dept Comp Sci & Technol |
推荐引用方式 GB/T 7714 | Xu, Y. W.,Cao, X. B.,Qiao, H.,et al. A cascaded classifier for pedestrian detection[C],2006. |
条目包含的文件 | 条目无相关文件。 |
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