College of Intelligence Science and Technology, National University of Defense Technology, Changsha 410073, China.
Science and Technology on Near-Surface Detection Laboratory, Wuxi 214035, China.
Sensors (Basel). 2021 Feb 7;21(4):1159. doi: 10.3390/s21041159.
Pedestrian detection plays an essential role in the navigation system of autonomous vehicles. Multisensor fusion-based approaches are usually used to improve detection performance. In this study, we aimed to develop a score fusion-based pedestrian detection algorithm by integrating the data of two light detection and ranging systems (LiDARs). We first evaluated a two-stage object-detection pipeline for each LiDAR, including object proposal and fine classification. The scores from these two different classifiers were then fused to generate the result using the Bayesian rule. To improve proposal performance, we applied two features: the central points density feature, which acts as a filter to speed up the process and reduce false alarms; and the location feature, including the density distribution and height difference distribution of the point cloud, which describes an object's profile and location in a sliding window. Extensive experiments tested in KITTI and the self-built dataset show that our method could produce highly accurate pedestrian detection results in real-time. The proposed method not only considers the accuracy and efficiency but also the flexibility for different modalities.
行人检测在自动驾驶车辆的导航系统中起着至关重要的作用。基于多传感器融合的方法通常用于提高检测性能。在本研究中，我们旨在通过整合两个激光雷达系统的数据，开发一种基于分数融合的行人检测算法。我们首先对每个激光雷达进行了两阶段目标检测管道的评估，包括目标提议和精细分类。然后，使用贝叶斯规则融合这两个不同分类器的分数来生成结果。为了提高提议的性能，我们应用了两个特征：中心点密度特征，它作为一个过滤器，可以加速处理过程并减少误报；以及位置特征，包括点云的密度分布和高度差分布，它描述了物体在滑动窗口中的轮廓和位置。在 KITTI 和自建数据集上进行的广泛实验表明，我们的方法可以实时生成高精度的行人检测结果。所提出的方法不仅考虑了准确性和效率，还考虑了不同模态的灵活性。
