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On-road Vehicle Detection And Tracking Based On Road Context And The Ambient Lighting Adaptive Framework

机译:基于道路环境和环境照明自适应框架的道路车辆检测与跟踪

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摘要

Despite recent active research on vision-based vehicle detection and tracking, its lack of flexibility hindered practical use in real environments. To be practical, adaptation to various illumination conditions is an essential ingredient. We propose a novel adaptation framework that can improve on this current lighting adaptation using a simple road context, feature arbiter, and a proper feature fusion scheme. In real driving environments, self-supervised online learning can efficiently segment the road and nonroad regions in front of the host vehicle. Classification into these regions is very important to generate regions of interest (ROIs) for potential vehicle position, that is, road context. It improves on system efficiency by reducing noise and processing time. In our global and local lighting models, the feature arbiter selects an appropriate daytime or nighttime detector for each ROI. And finally, an adaptive fusion framework method can robustly track by selecting or removing the distinctive visual attributes. This system was successfully tested on real road data obtained with various ambient lighting conditions.
机译:尽管最近对基于视觉的车辆检测和跟踪进行了积极的研究,但是其缺乏灵活性阻碍了其在实际环境中的实际使用。实际上,适应各种照明条件是必不可少的。我们提出了一种新颖的适应框架,可以使用简单的道路环境,特征仲裁器和适当的特征融合方案来改进当前的光照适应。在实际的驾驶环境中,自我监督的在线学习可以有效地对本车前方的道路和非道路区域进行分割。对这些区域进行分类对于生成潜在的车辆位置(即道路环境)感兴趣区域(ROI)非常重要。它通过减少噪声和处理时间来提高系统效率。在我们的全球和本地照明模型中,功能仲裁器为每个ROI选择适当的白天或夜间检测器。最后,自适应融合框架方法可以通过选择或删除独特的视觉属性来进行稳健的跟踪。该系统已在各种环境照明条件下获得的真实道路数据上成功进行了测试。

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