Detection of pedestrians at night time using learning-based method and head validation

Qiong Liu, Jiajun Zhuang, Shufeng Kong
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引用次数: 14

Abstract

To improve automotive active safety and guarantee the safety of pedestrians at night time, a fast pedestrian detection method based on monocular far-infrared camera for driver assistance systems is proposed. According to the distribution of gray-level intensity of pedestrian samples, an adaptive local dual threshold segmentation algorithm is executed first to extract candidate regions. The presented pedestrian detector uses histograms of oriented gradients (HOG) as features and support vector machine (SVM) as classifier. In order to speed up the classification phase, the resulting support vectors (SVs) obtained by SVM is optimized to reduce the number of SVs used for decision. A further validation p hase is then introduced to filter the false alarms according to the distribution of gray-level intensity of pedestrians' heads. Experimental results show that the proposed method performs as fast as 34 frames per second on average and guarantees a real-time pedestrian detection; the whole system produces a detection rate of 84.83% at the cost of less than 4% false alarm rate on suburban scenes while produces a detection rate of about 81% at the cost of lower than 10% false alarm rate on urban scenes.
基于学习方法和头部验证的夜间行人检测
为了提高汽车的主动安全性,保证夜间行人的安全,提出了一种基于单目远红外摄像机的驾驶员辅助系统行人快速检测方法。根据行人样本灰度强度的分布,首先采用自适应局部双阈值分割算法提取候选区域;该行人检测器采用梯度直方图(HOG)作为特征,支持向量机(SVM)作为分类器。为了加快分类阶段,对支持向量机得到的支持向量进行优化,减少用于决策的支持向量个数。然后引入进一步的验证阶段,根据行人头部灰度强度的分布来过滤虚警。实验结果表明,该方法平均可达34帧/秒,保证了行人检测的实时性;整个系统在郊区场景以低于4%的虚警率为代价,检测率达到84.83%;在城市场景以低于10%的虚警率为代价,检测率达到81%左右。
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