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首页> 外文会议>2011 9th World Congress on Intelligent Control and Automation : Conference Digest >Pedestrian detection in complex scene using full binary tree classifiers based on locally assembled Binary Haar-like features
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Pedestrian detection in complex scene using full binary tree classifiers based on locally assembled Binary Haar-like features

机译：使用基于本地组装的二进制Haar类特征的完整二叉树分类器在复杂场景中进行行人检测

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Under complex scene urban environment, in order to detect pedestrians efficiently and accurately, we propose a high real-time and robust performance pedestrian detection method based on machine vision in this paper. Firstly, a new feature called Locally Assembled Binary Haar-like (LABH) is selected as the feature vector. In this novel feature, Haar features keep only the ordinal relationship named by binary Haar feature, then, brings in similar idea of Local Binary Patter (LBP), assemble several neighboring binary Haar feature to improve the ability of illumination invariant and discriminating power. Furthermore, a full binary tree structure is presented to build on an efficient classifier, which has advantages of both series connection structure and parallel connection structure and brings in a principle of “Early-rejection”, could improve system''s real-time performance. The experiment carried out on videos from INRIA dataset, MIT dataset and Daimler dataset illustrates that the proposed method is real-time and feasible enough for pedestrian detection in intelligent vehicle environment.
机译：在复杂场景的城市环境下，为了有效，准确地检测出行人，本文提出了一种基于机器视觉的实时性强，性能高的行人检测方法。首先，选择一个称为局部组装二进制Haar-like（LABH）的新特征作为特征向量。在这个新颖的特征中，Haar特征仅保留以二进制Haar特征命名的序数关系，然后引入类似的局部二进制模式（LBP）的思想，组装几个相邻的二进制Haar特征以提高照明不变性和判别能力的能力。此外，提出了一种基于高效分类器的完整二叉树结构，该结构具有串联连接结构和并联连接结构的优点，并引入了“早期拒绝”原理，可以提高系统的实时性能。 。对来自INRIA数据集，MIT数据集和Daimler数据集的视频进行的实验表明，该方法是实时的，对于智能车辆环境中的行人检测来说足够可行。

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