基于场景模型与统计学习的鲁棒行人检测算法

提出一种基于场景模型和统计学习的行人检测算法. 针对训练行人检测器时面临的动态场景的复杂性和行人样本多样性等问题, 通过背景建模, 从场景的背景图像上提取有限的负样本用于训练, 大幅度提高了分类器的检测率, 同时降低了虚警; 提出一种快速弱分类器选择算法, 根据正、负样本特征大小的分布和期望的检测率, 直接求解特征大小的阈值范围, 能够满足在线训练和更新检测器的要求; 提出一种基于正样本错误率的训练算法, 先根据正样本加权错误率选择弱分类器, 快速提高检测率, 在训练结束后调整最终分类器的加权系数, 在保证检测率的同时尽可能降低虚警率. 实验中构建了一个试验视频数据库和行人样本库, 数据库包括雨、雪、阴影、季节变化、摄像机平移、旋转、缩放等情况, 并设计实现了一个实时行人检测系统BMAT (Background modeling and Adaboost training), 实验结果证明了算法的有效性.

Scene Modeling and Statistical Learning Based Robust Pedestrian Detection Algorithm

A scene model and statistic learning based method for pedestrian detection in complicated real-world scenes is proposed. A unique characteristic of the algorithm is its ability to train a special cascade classifier dynamically for each individual scene. The benefit is that the classifier only focuses on the differences between the positive samples and the limited negative samples of each individual scene, thus greatly reduces the complexity of classification, and achieves robust detection result even with a few classifiers. A highly efficient weak classifier selection method and a novel boosting architecture are presented to speed up feature selection and classifier training. To evaluate the proposed algorithm, we captured pedestrian videos under different weathers, seasons and camera motions, and labeled 4300 positive samples. Moreover, a real-time pedestrian detection system named as background modeling and Adaboost training (BMAT) was developed, which produced fast and robust detection results as demonstrated by extensive experiments performed using video sequences under different environments.