Passenger Location Estimation in Public Transport: Evaluating Methods and Camera Placement Impact

In dataset related to our article, we provide two types of data - detections of the calibration marker - called later on "Regression Data" and detections of people inside and outside the model - called later on "In_Out_People". Abstract: The application of image analysis methods to calculate the distance from the camera to the object allows the replacement of specialized hardware devices for distance estimation. In the case of public transport, estimation of the exact position of the passenger gives also the option to determine whether the passenger is inside or outside the vehicle. In the presented work, several distance estimation methods based on typical analytical models and machine learning (ML) methods were tested using recordings from three cameras located in the minibus model. Human head detection was used instead of the entire passenger body to avoid occlusion problems. The analytical method showed worse performance than ML methods in all cases. The difference in the performance of ML models between cameras was negligible and there was no best method found. The computational time for ML models ranges from 0.35 to 100.57 ms, which should result in successful real-world applications. The developed approach can be used not only in public transport but also in all closed areas for the calculation of people or crowd density.本论文配套数据集包含两类数据：校准标记检测数据（后文称为"回归数据（Regression Data）"），以及车内、车外人员检测数据（后文称为"In_Out_People"）。 摘要： 将图像分析方法应用于相机与物体间距离的计算，可替代用于距离估计的专用硬件设备。在公共交通场景中，精准估算乘客的位置，还可判断乘客处于车辆内部还是外部。 本研究针对基于典型解析模型与机器学习（Machine Learning，简称ML）方法的多种距离估计算法，使用安装在微型巴士模型上的三台摄像头采集的视频片段开展了测试。为规避遮挡问题，研究采用人头检测而非完整乘客躯体作为检测目标。 实验结果表明，在所有测试场景中，解析类方法的性能均劣于机器学习方法。不同摄像头对应的机器学习模型性能差异可忽略不计，且未找到最优的单一算法。 机器学习模型的计算耗时区间为0.35 ms至100.57 ms，足以满足实际落地应用的实时性要求。本研究所提出的方案不仅可应用于公共交通场景，还可推广至所有封闭区域，用于人员或人群密度的测算。