基于单目视频和无监督学习的轻轨定位方法

基于视觉信息的场景识别定位模块被广泛应用于车辆安全系统。针对目前场景逐帧匹配算法训练数据量大、匹配处理计算复杂度高以及跟踪精度低导致难以实际应用的问题，该文提出一种新的基于局部关键区域与关键帧的场景识别方法，在保证匹配精度的同时满足系统实时性的要求。首先，该方法仅使用单目摄像机捕获的单一序列作为参考序列，采用无监督方式提取序列的显著性区域作为关键区域，并计算关键区域中低相关性的二值化特征，提高了场景匹配的精确度并大幅减少了实时场景匹配过程中特征生成与匹配的计算复杂度。其次，该方法以显著性分数为依据提取参考序列中的关键帧，缩小了跟踪模块的检索范围并提高了检索效率。该文使用香港轻轨系统数据集以及公开测试数据集进行方法测试。实验结果表明，该文方法在实现快速匹配的同时，其匹配正确率较基于全局特征匹配方法SeqSLAM提高了9.8%。

Learning-based Localization with Monocular Camera for Light-rail System

The visual-based scene recognition and localization module is widely used in vehicle safety system. This paper proposes a new method of scene recognition based on local key region and key frame, which is based on the problem of large amount of training data, large matching complexity and low tracking precision. The proposed method meets the real-time requirements with high accuracy. First, the method uses the unsupervised method to extract the significant regions of the single reference sequence captured by the monocular camera as the key regions. The binary features with low correlation in key regions are also extracted to improve the scene matching accuracy and reduce the computational complexity of feature generation and matching. Secondly, key frames in the reference sequence are extracted based on the discrimination score to reduce the retrieval range of the tracking module and improve the efficiency. Practical field tests are done on real data of the light railway system in Hong Kong and the open test data set in Nordland. The experimental results show that the proposed method achieves fast matching and the precision is 9.8% higher than SeqSLAM which is based on global feature.