作者:
Hariyono， J. and Jo， K.-H.
关键词:
Roads； Legged locomotion； Computer vision； Optical imaging； Image motion analysis； Vehicles； Image color analysis
摘要:
Detection of pedestrian crossing road is described in this paper. Single camera is used to detect pedestrians, thus classify them as a pedestrian crossing road or not. The moving pedestrian is detected using improved sparse optical flow method. The proposed technique consists of three main components. First, overlapping blocks are applied in consecutive images. KLT tracker is used to find corresponding corner feature in consecutive images. Second, classify each block into motion region (foreground) and background, where each block is processed by a cascade composed of three classifiers. Third, probabilistic generation of the foreground mask is performed. The classification decisions for all blocks are integrated into final pixel-level foreground segmentation. In order to classify the pedestrian crossing road, a walking human model is proposed. It is calculated by the region volume of the detected bounding box. A walking human is defined as the ratio of the width divided by the height of the detected bounding box. To be sure that the moving object is a walking human, ratio of the centroid location from the ground plane divided by the height of bounding box should satisfy a constraint. The proposed algorithms are evaluated using publicly (Caltech and ETH) datasets and our real world driving data. The performance result shows the correct pedestrian detection rate is 99.50% at 0.09 false positive per image. The pedestrian crossing road classification shown correct detection rate is 98.10%.
在线下载