Real-time pedestrian detection using eigenflow

被引:0
作者
Goel, Dhiraj [1 ]
Chen, Tsuhan [1 ]
机构
[1] Carnegie Mellon Univ, Dept Elect & Comp Engn, Pittsburgh, PA 15213 USA
来源
2007 IEEE INTERNATIONAL CONFERENCE ON IMAGE PROCESSING, VOLS 1-7 | 2007年
关键词
optical flow; PCA; AdaBoost;
D O I
暂无
中图分类号
TM [电工技术]; TN [电子技术、通信技术];
学科分类号
0808 ; 0809 ;
摘要
We propose a novel learning algorithm to detect moving pedestrians from a stationary camera in, real-time. The algorithm learns a discriminative model based on eigenflow, i.e., the eigenvectors derived from applying Principal Component Analysis to the optical flow of moving objects, to differentiate between human motion patterns from other kind of motions like of cars etc. The learned model is a cascade of Adaboost classifiers of increasing complexity, with eigenflow vectors as the weak classifiers. Unlike some recent attempts to use motion for pedestrian detection, this system works in real-time. Moreover, the system is robust to small camera motion and slow illumination changes, and can detect moving children even though the training data had only adult pedestrians.
引用
收藏
页码:1357 / 1360
页数:4
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