车载热成像行人检测RoI提取方法

热成像适合低照度行人检测，车载热成像背景灰度分布变化大，行人目标易与背景干扰物混淆，捕捉远距离行人困难，以双阈值分割方法提取RoI(region of interest)很难满足系统召回率和虚警率要求.针对车载热成像行人检测，提出新的RoI提取方法，包括图像预处理、RoI提取和后处理.设计膨胀最大值滤波器进行图像增强；利用Haar-like特征来改进自适应双阈值分割法进行RoI提取，设计增量计算模型以提高计算效率；设计目标时序特性和空间对称性过滤器来排除虚假RoI.与基准方法相比，当虚警率不高于40时，本文方法提高召回率49%，且RoI提取速率不低于18帧/s.

RoI Extraction for Vehicular Thermal Infrared Pedestrian Detection

Thermal infrared images are suitable for pedestrian detection in low illumination such as at night. The grayscale distribution of background in an infrared image vehicle-mounted varies obviously and pedestrian is easily confused with background interference and it is difficult to catch a pedestrian in the distance. The system recall rate and false alarm rate requirements can’t be achieved through extracting RoI with double threshold segmentation method. We construct a new RoI extraction method consisting of image preprocessing， RoI generation and RoI post processing etc. An expanding maximum filter is designed to enhance image contrast. Adaptive double threshold segmentation is improved by Haar-like feature. Computing efficiency is raised by designing incremental model. Besides， filters considering gray-scale temporal feature and spatial symmetry feature of a pedestrian are presented to remove false RoI. Comparing with the benchmark method， our method improves recall rate by 49% when the number of false RoIs is less than 40 per frame. And RoI extraction speed isn’t lower than 18 frames per second.