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Study on pedestrian detection and tracking with monocular vision

机译:单眼视觉行人检测与跟踪研究

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Pedestrian detection is a rapidly evolving topic in many computer vision applications such as intelligent vehicle, surveillance and advanced robotics. The pedestrian collision avoidance not only requires detection of pedestrian but also requires prediction by tracking to analyze its dynamics and behaviors. The objective of this paper is to provide a method realizing pedestrian detection and tracking based on monocular vision. The first part of the paper is to detect pedestrian from the image. Both the rectangle features and edge orientation features are calculated by integral image techniques and Adaboost is used to fulfill discriminative features selection and classifiers training. The second part contains a pedestrian tracking method based on Kalman filtering. Experiments are performed to test and verify the pedestrian detection and tracking method under normal urban environments. The experiment results show that the method can detect and track pedestrian ahead of vehicle with different sizes and postures.
机译:在许多计算机视觉应用程序中,例如智能车辆,监视和先进的机器人技术,行人检测是一个快速发展的主题。避免行人碰撞不仅需要检测行人,还需要通过跟踪进行预测以分析其动态和行为。本文的目的是提供一种基于单眼视觉实现行人检测与跟踪的方法。本文的第一部分是从图像中检测行人。矩形特征和边缘方向特征均通过积分图像技术计算,而Adaboost用于完成区分特征的选择和分类器训练。第二部分包含基于卡尔曼滤波的行人跟踪方法。进行实验以测试和验证正常城市环境下的行人检测和跟踪方法。实验结果表明,该方法可以检测和跟踪不同大小和姿势的车辆前方行人。

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