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Body Parts Features Based Pedestrian Detection for Active Pedestrian Protection System

机译:主动行人保护系统基于人体特征的行人检测

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摘要

A novel pedestrian detection system based on vision in urban traffic situations is presented to help the driver perceive the pedestrian ahead of vehicle. To enhance the accuracy and to decrease the time consumption of pedestrian detection in such complicated situations, the pedestrian is detected by dividing it into several parts according to their corresponding features in the image. The candidate pedestrian leg is segmented based on the gentle Adaboost algorithm by training the optimized histogram of gradient features. The candidate pedestrian head is located by matching the pedestrian head and shoulder model above the region of the candidate leg. Then the candidate leg, head and shoulder are combined by parts constraint and threshold adjustment to verify the existence of pedestrian. Experiments in real urban traffic circumstances were conducted finally. Results show that the proposed pedestrian detection method can achieve a pedestrian detection rate of 92.1% with less time consumption.
机译:提出了一种基于视觉的城市交通情况下的行人检测系统,以帮助驾驶员感知车辆前方的行人。为了提高精度并减少这种复杂情况下行人检测的时间消耗,通过根据行人在图像中的相应特征将行人分为几部分来检测行人。通过训练优化的梯度特征直方图,基于温和的Adaboost算法对候选行人腿进行分段。通过在候选腿部区域上方匹配行人头肩模型来定位候选行人头。然后通过部分约束和阈值调整将候选的腿,头和肩膀组合在一起,以验证行人的存在。最后进行了实际城市交通环境下的实验。结果表明,所提出的行人检测方法能够以较少的时间消耗实现92.1%的行人检测率。

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