用公交摄像头实现抗遮挡的乘客身份追踪，提升出行数据采集精度。
TransitReID: Transit OD Data Collection with Occlusion-Resistant Dynamic Passenger Re-Identification
- 结合变分自编码器与注意力机制，聚焦可见身体区域提升识别鲁棒性。
- 在真实公交运行中实现80%-90%的出行轨迹匹配准确率。
- 支持边缘设备实时处理，保护隐私且可规模化部署。
公共交通起讫点（OD）数据对优化服务至关重要，但现有方法如人工调查、蓝牙/WiFi追踪和自动计数器成本高、依赖设备或无法实现个体匹配。本文利用已部署于多数公交车辆的监控摄像头，提出TransitReID框架，实现抗遮挡的个体级乘客重识别（ReID）。该框架包含三项核心组件：(1) 基于变分自编码器引导的区域注意力机制与选择性特征池化，增强对可见且具判别性的身体区域的关注；(2) 分层存储与动态匹配（HSDM）机制，适应动态公交运行，兼顾准确性、内存与速度；(3) 多线程边缘实现，通过本地数据处理实现近实时OD估计并保障隐私。我们还构建了新数据集，含超过17,000张真实公交前后摄像头拍摄的图像，涵盖多种遮挡与视角条件。实验表明，TransitReID在所提数据集上达到88.3%的R-1准确率，在仿真与真实运行中均保持80%-90%的OD估计准确率，且可在NVIDIA Jetson边缘设备上部署。本工作为可扩展、隐私保护的自动化公交OD数据采集提供了算法与系统基础。
原文摘要 · Abstract (English)
Transit Origin-Destination (OD) data are fundamental for optimizing public transit services, yet current collection methods, such as manual surveys, Bluetooth/WiFi tracking, and Automated Passenger Counters, are often costly, device-dependent, or unable to support individual-level matching. Meanwhile, onboard surveillance cameras already deployed on most transit vehicles provide an underutilized opportunity for automated OD data collection. Leveraging this, we present TransitReID, a framework for individual-level and occlusion-resistant passenger re-identification (ReID) tailored to transit environments. TransitReID introduces three key components: (1) an occlusion- and viewpoint-robust ReID algorithm that integrates a variational autoencoder-guided region-attention mechanism with selective feature pooling to emphasize visible and discriminative body regions; (2) a Hierarchical Storage and Dynamic Matching (HSDM) mechanism that adapts static ReID matching to dynamic bus operations while balancing accuracy, memory, and speed; and (3) a multi-threaded edge implementation that enables near real-time OD estimation while preserving privacy through local data processing. We also construct a new Transit ReID dataset with over 17,000 images captured from real bus front/rear cameras under diverse occlusion and viewpoint conditions. Experimental results show that TransitReID achieves state-of-the-art ReID performance, attaining 88.3% R-1 accuracy on the proposed transit ReID dataset and sustaining 80-90% OD estimation accuracy in both simulations and real-world operation, with deployment supported on NVIDIA Jetson edge devices. This work provides an algorithmic and system-level foundation for scalable, privacy-preserving automated transit OD collection.
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