Pedestrian Perception Method Based on Infrared Stereo Vision
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摘要: 基于热红外特性，红外立体视觉路况行人感知方法可以在夜间、雾霾环境下有效检测道路场景中的行人等目标，提高驾驶安全性。针对红外图像中纹理细节少，传统稠密双目立体匹配算法效果差的问题，本文首先根据目标在红外图像下的亮度、边缘特征提取感兴趣区域（Region of interest, ROI）；然后在ROI中提取图像特征点并匹配，进而计算原始稀疏深度图；最后根据目标表面深度变化较小的特点，结合ROI和原始深度图估计半稠密深度图。本文搭建了实验系统验证该方法的有效性。实验结果表明，在系统约120°观测视场角内，该方法对行人等目标深度感知相对误差在15 m范围内优于1.5%，30 m范围内优于3%。Abstract: Based on the thermal infrared characteristics, the infrared stereo vision pedestrian perception method can effectively detect and measure pedestrians in road scenes at night and hazy environments, with the aim of improving driving safety. Owing to less texture details in infrared images, the traditional dense binocular stereo matching algorithm performs poorly. To solve this problem, the region of interest (ROI) is extracted according to the brightness and edge features of the targets in the infrared image. Then, the image feature points are extracted and matched in the ROI to calculate the original sparse depth map. Finally, according to the small depth difference in the surface of the targets, the semi-dense depth map was estimated by combining the ROI and the original depth map. We designed an experimental system to verify the effectiveness of the proposed method. The experimental results showed that the relative error of the depth perception of pedestrians was better than 1.5% at 15 m and 3% at 30 m in the field of view of approximately 120°.
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Keywords:
- long-wave infrared /
- stereo vision /
- SURF /
- depth map /
- assisted driving
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图 3 大视场红外立体视觉实验系统
Figure 3. Large field of view infrared stereo vision experimental system
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图 10 左视双目视场目标参考位置
Figure 10. Reference position of targets in left binocular field of view
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