Ibarra-Arenado Manuel, Tjahjadi Tardi, Pérez-Oria Juan, Robla-Gómez Sandra, Jiménez-Avello Agustín
Control Engineering Group, University of Cantabria, Avda. Los Castros s/n, 39005 Santander, Spain.
School of Engineering, University of Warwick, Gibbet Hill Road, Coventry CV4 7AL, UK.
Sensors (Basel). 2017 Apr 27;17(5):975. doi: 10.3390/s17050975.
Vehicle detection is a fundamental task in Forward Collision Avoiding Systems (FACS). Generally, vision-based vehicle detection methods consist of two stages: hypotheses generation and hypotheses verification. In this paper, we focus on the former, presenting a feature-based method for on-road vehicle detection in urban traffic. Hypotheses for vehicle candidates are generated according to the shadow under the vehicles by comparing pixel properties across the vertical intensity gradients caused by shadows on the road, and followed by intensity thresholding and morphological discrimination. Unlike methods that identify the shadow under a vehicle as a road region with intensity smaller than a coarse lower bound of the intensity for road, the thresholding strategy we propose determines a coarse upper bound of the intensity for shadow which reduces false positives rates. The experimental results are promising in terms of detection performance and robustness in day time under different weather conditions and cluttered scenarios to enable validation for the first stage of a complete FACS.
车辆检测是前碰撞避免系统（FACS）中的一项基本任务。一般来说，基于视觉的车辆检测方法包括两个阶段：假设生成和假设验证。在本文中，我们专注于前者，提出一种基于特征的城市交通道路车辆检测方法。通过比较道路上阴影引起的垂直强度梯度上的像素属性，根据车辆下方的阴影生成车辆候选假设，然后进行强度阈值处理和形态学判别。与将车辆下方的阴影识别为强度小于道路强度粗略下限的道路区域的方法不同，我们提出的阈值策略确定了阴影强度的粗略上限，从而降低了误报率。实验结果在不同天气条件和杂乱场景下的白天检测性能和鲁棒性方面很有前景，可为完整FACS的第一阶段提供验证。
