一种基于激光与视频信息时空数据融合的行人检测方法

针对城市交通行人安全问题，本文提出了一种基于激光与视频数据融合的行人检测方法.通过激光与视频数据空间和时间上的融合，将激光数据映射到图像坐标；在激光聚类过程中，采用K-means 聚类算法对激光云点进行聚类分析，然后运用行人宽度模型提取候选行人区域；在基于图像的行人检测过程中，选取头肩、躯干以及腿部人体特征部位，采用Haar-like 特征集和Boosting 算法进行训练，得到部位检测器；最后，基于贝叶斯决策的组合策略对候选行人区域进行有效判定.实验结果表明，本文所述算法有较好的检测精度和实时性能.

A Pedestrian Detection Method under Time-space Data Fusion Based on Laser and Video Information

A pedestrian detection method based on laser and video information fusion is proposed concerning the pedestrian safety problem in urban traffic. Laser data are projected to image coordinate system through spatial and temporal combination of laser and video data. In the process of laser clustering, Kmeans clustering algorithm is adopted to conduct clustering analysis on laser point clouds, while pedestrian width model is employed to extract candidate pedestrian region. In the process of pedestrian detection, characteristic part of pedestrian such as head-shoulders, body and legs are selected, and Haar-like feature is adopted and trained through Boosting algorithm. The obtained part detector is used to detect pedestrian, deciding the validity of candidate pedestrian region through composition strategy based on Bayesian decision. The result of the experiment shows that the proposed algorithm has preferable real-time and detection performance.