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Information fusion to detect and classify pedestrians using invariant features

机译：信息融合使用不变特征对行人进行检测和分类

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摘要

A novel approach to detect pedestrians and to classify them according to their moving direction and relative speed is presented in this paper. This work focuses on the recognition of pedestrian lateral movements, namely: walking and running in both directions, as well as no movement. The perception of the environment is performed through a lidar sensor and an infrared camera. Both sensor signals are fused to determine regions of interest in the video data. The classification of these regions is based on the extraction of 2D translation invariant features, which are constructed by integrating over the transformation group. Special polynomial kernel functions are defined in order to obtain a good separability between the classes. Support vector machine classifiers are used in different configurations to classify the invariants. The proposed approach was evaluated offline considering fixed sensors. Results obtained based on real traffic scenes demonstrate very good detection and classification rates.
机译：本文提出了一种新的检测行人的方法，并根据行人的行进方向和相对速度对其进行分类。这项工作着重于对行人横向运动的识别，即：双向行走和奔跑以及无运动。通过激光雷达传感器和红外热像仪进行环境感知。将两个传感器信号融合以确定视频数据中的感兴趣区域。这些区域的分类基于二维平移不变特征的提取，这些特征是通过对变换组进行积分而构造的。定义了特殊的多项式内核函数，以便在类之间获得良好的可分离性。支持向量机分类器用于不同的配置中以对不变量进行分类。考虑到固定传感器，对提出的方法进行了离线评估。根据实际交通场景获得的结果显示出很好的检测率和分类率。

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