TransitReID: Transit OD Data Collection with Occlusion-Resistant Dynamic Passenger Re-Identification

• URL: http://arxiv.org/abs/2504.11500v2
• Date: Wed, 10 Sep 2025 01:00:18 GMT
• Title: TransitReID: Transit OD Data Collection with Occlusion-Resistant Dynamic Passenger Re-Identification
• Authors: Kaicong Huang, Talha Azfar, Jack Reilly, Ruimin Ke,
• Abstract summary: We present TransitReID, a novel framework for individual-level and occlusion-resistant passenger re-identification.<n>This work advances both the algorithmic and system-level foundations of automated transit OD collection.
• Score: 1.5119440099674915
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Transit Origin-Destination (OD) data are fundamental for optimizing public transit services, yet current collection methods, such as manual surveys, Bluetooth and WiFi tracking, or Automated Passenger Counters, are either costly, device-dependent, or incapable of 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 novel framework for individual-level and occlusion-resistant passenger re-identification tailored to transit environments. Our approach introduces three key innovations: (1) an occlusion-robust ReID algorithm that integrates a variational autoencoder-guided region-attention mechanism and selective quality feature averaging to dynamically emphasize visible and discriminative body regions under severe occlusions and viewpoint variations; (2) a Hierarchical Storage and Dynamic Matching HSDM mechanism that transforms static gallery matching into a dynamic process for robustness, accuracy, and speed in real-world bus operations; and (3) a multi-threaded edge implementation that enables near real-time OD estimation while ensuring privacy by processing all data locally. To support research in this domain, we also construct a new TransitReID dataset with over 17,000 images captured from bus front and rear cameras under diverse occlusion and viewpoint conditions. Experimental results demonstrate that TransitReID achieves state-of-the-art performance, with R-1 accuracy of 88.3 percent and mAP of 92.5 percent, and further sustains 90 percent OD estimation accuracy in bus route simulations on NVIDIA Jetson edge devices. This work advances both the algorithmic and system-level foundations of automated transit OD collection, paving the way for scalable, privacy-preserving deployment in intelligent transportation systems.

Related papers

• Optimization-Guided Diffusion for Interactive Scene Generation [52.23368750264419]
  We present OMEGA, an optimization-guided, training-free framework that enforces structural consistency and interaction awareness during diffusion-based sampling.<n>We show that OMEGA improves generation realism, consistency, and controllability, increasing the ratio of physically and behaviorally valid scenes.<n>Our approach can also generate $5times$ more near-collision frames with a time-to-collision under three seconds.
  arXiv  Detail & Related papers  (2025-12-08T15:56:18Z)
• Multi-Agent Coordination in Autonomous Vehicle Routing: A Simulation-Based Study of Communication, Memory, and Routing Loops [0.5801044612920815]
  We introduce Object Memory Management (OMM), a lightweight mechanism enabling agents to retain and share knowledge of obstacles.<n>OMM operates by maintaining a distributed blacklist of blocked nodes, which each agent consults during Dijkstra-based path recalculation.<n>Our results show that OMM-enabled coordination reduces average travel time by 75.7% and wait time by 88% compared to memory-less systems.
  arXiv  Detail & Related papers  (2025-11-20T17:42:49Z)
• Lane-Wise Highway Anomaly Detection [8.086502588472783]
  This paper proposes a scalable and interpretable framework for lane-wise highway traffic anomaly detection.<n>Unlike traditional sensor-dependent methods, our approach uses AI-powered vision models to extract lane-specific features.<n>Our framework outperforms state-of-the-art methods in precision, recall, and F1-score.
  arXiv  Detail & Related papers  (2025-05-05T12:32:23Z)
• Optimal Transport Adapter Tuning for Bridging Modality Gaps in Few-Shot Remote Sensing Scene Classification [80.83325513157637]
  Few-Shot Remote Sensing Scene Classification (FS-RSSC) presents the challenge of classifying remote sensing images with limited labeled samples.<n>We propose a novel Optimal Transport Adapter Tuning (OTAT) framework aimed at constructing an ideal Platonic representational space.
  arXiv  Detail & Related papers  (2025-03-19T07:04:24Z)
• DriveTransformer: Unified Transformer for Scalable End-to-End Autonomous Driving [62.62464518137153]
  DriveTransformer is a simplified E2E-AD framework for the ease of scaling up.<n>It is composed of three unified operations: task self-attention, sensor cross-attention, temporal cross-attention.<n>It achieves state-of-the-art performance in both simulated closed-loop benchmark Bench2Drive and real world open-loop benchmark nuScenes with high FPS.
  arXiv  Detail & Related papers  (2025-03-07T11:41:18Z)
• Towards Intelligent Transportation with Pedestrians and Vehicles In-the-Loop: A Surveillance Video-Assisted Federated Digital Twin Framework [62.47416496137193]
  We propose a surveillance video assisted federated digital twin (SV-FDT) framework to empower ITSs with pedestrians and vehicles in-the-loop.<n>The architecture consists of three layers: (i) the end layer, which collects traffic surveillance videos from multiple sources; (ii) the edge layer, responsible for semantic segmentation-based visual understanding, twin agent-based interaction modeling, and local digital twin system (LDTS) creation in local regions; and (iii) the cloud layer, which integrates LDTSs across different regions to construct a global DT model in realtime.
  arXiv  Detail & Related papers  (2025-03-06T07:36:06Z)
• AirTag, You're It: Reverse Logistics and Last Mile Dynamics [0.0]
  This study addresses challenges in reverse logistics, particularly in disaster relief scenarios where infrastructure disruptions demand adaptive solutions.<n>Using 20 Apple AirTags embedded in packages, this research provides empirical insights into logistical flows, capturing granular spatial and temporal data through Bluetooth LE (BLE) 5 trackers integrated with the Apple Find My network.<n>These trackers demonstrated their value in monitoring dynamic cargo movements, enabling real-time adjustments in mobile hub placement and route optimization, particularly in disaster relief contexts like Hurricane Helene.
  arXiv  Detail & Related papers  (2025-02-03T02:21:23Z)
• DEEGITS: Deep Learning based Framework for Measuring Heterogenous Traffic State in Challenging Traffic Scenarios [0.0]
  This paper presents DEEGITS (Deep Heterogeneous Traffic State Measurement), a comprehensive framework that leverages state-of-the-art convolutional neural network (CNN) techniques to accurately and rapidly detect vehicles and pedestrians. In this study, we enhance the training dataset through data fusion, enabling simultaneous detection vehicles and pedestrians. The framework is tested to measure heterogeneous traffic states in mixed traffic conditions.
  arXiv  Detail & Related papers  (2024-11-13T04:49:32Z)
• Reliable, Routable, and Reproducible: Collection of Pedestrian Pathways at Statewide Scale [7.346075203371274]
  This paper presents a methodology to collect, manage, serve, and maintain pedestrian path data at a statewide scale. We aim to produce routable pedestrian pathways for the entire State of Washington within approximately two years.
  arXiv  Detail & Related papers  (2024-10-12T02:31:57Z)
• Adaptive Data Transport Mechanism for UAV Surveillance Missions in Lossy Environments [2.700610024690147]
  Unmanned Aerial Vehicles (UAVs) play an increasingly critical role in Intelligence, Surveillance, and Reconnaissance (ISR) missions. This paper offers an alternative AI-driven scheduling policy that prioritizes selecting regions of the image that significantly contributes to the mission objective.
  arXiv  Detail & Related papers  (2024-09-30T18:22:58Z)
• DiFSD: Ego-Centric Fully Sparse Paradigm with Uncertainty Denoising and Iterative Refinement for Efficient End-to-End Self-Driving [55.53171248839489]
  We propose an ego-centric fully sparse paradigm, named DiFSD, for end-to-end self-driving.<n>Specifically, DiFSD mainly consists of sparse perception, hierarchical interaction and iterative motion planner.<n>Experiments conducted on nuScenes and Bench2Drive datasets demonstrate the superior planning performance and great efficiency of DiFSD.
  arXiv  Detail & Related papers  (2024-09-15T15:55:24Z)
• DSDFormer: An Innovative Transformer-Mamba Framework for Robust High-Precision Driver Distraction Identification [23.05821759499963]
  Driver distraction remains a leading cause of traffic accidents, posing a critical threat to road safety globally. We propose DSDFormer, a framework that integrates the strengths of Transformer and Mamba architectures. We also introduce Temporal Reasoning Confident Learning (TRCL), an unsupervised approach that refines noisy labels by leveragingtemporal correlations in video.
  arXiv  Detail & Related papers  (2024-09-09T13:16:15Z)
• Leveraging Driver Field-of-View for Multimodal Ego-Trajectory Prediction [69.29802752614677]
  RouteFormer is a novel ego-trajectory prediction network combining GPS data, environmental context, and the driver's field-of-view.<n>To tackle data scarcity and enhance diversity, we introduce GEM, a dataset of urban driving scenarios enriched with synchronized driver field-of-view and gaze data.
  arXiv  Detail & Related papers  (2023-12-13T23:06:30Z)
• Elastic Interaction Energy-Informed Real-Time Traffic Scene Perception [8.429178814528617]
  A topology-aware energy loss function-based network training strategy named EIEGSeg is proposed. EIEGSeg is designed for multi-class segmentation on real-time traffic scene perception. Our results demonstrate that EIEGSeg consistently improves the performance, especially on real-time, lightweight networks.
  arXiv  Detail & Related papers  (2023-10-02T01:30:42Z)
• Unsupervised Domain Adaptation for Self-Driving from Past Traversal Features [69.47588461101925]
  We propose a method to adapt 3D object detectors to new driving environments. Our approach enhances LiDAR-based detection models using spatial quantized historical features. Experiments on real-world datasets demonstrate significant improvements.
  arXiv  Detail & Related papers  (2023-09-21T15:00:31Z)
• SDR-GAIN: A High Real-Time Occluded Pedestrian Pose Completion Method for Autonomous Driving [2.6663666678221376]
  We propose a novel real-time occluded pedestrian pose completion framework Separation and Dimensionality Reduction-based Generative Adrial Imputation Nets (SDR-GAIN)<n>SDR-GAIN aims to learn human pose directly from the numerical distribution of keypoint coordinates and interpolate missing positions.<n>Experiments conducted on the COCO and JAAD datasets demonstrate that SDR-GAIN surpasses conventional machine learning and Transformer-based missing data algorithms.
  arXiv  Detail & Related papers  (2023-06-06T09:35:56Z)
• Communication-Critical Planning via Multi-Agent Trajectory Exchange [21.923724399511798]
  This paper addresses the task of joint multi-agent perception and planning. It relates to the real-world challenge of collision-free navigation for connected self-driving vehicles.
  arXiv  Detail & Related papers  (2023-03-10T16:59:24Z)
• Scalable Vehicle Re-Identification via Self-Supervision [66.2562538902156]
  Vehicle Re-Identification is one of the key elements in city-scale vehicle analytics systems. Many state-of-the-art solutions for vehicle re-id mostly focus on improving the accuracy on existing re-id benchmarks and often ignore computational complexity. We propose a simple yet effective hybrid solution empowered by self-supervised training which only uses a single network during inference time.
  arXiv  Detail & Related papers  (2022-05-16T12:14:42Z)
• On the Role of Multi-Objective Optimization to the Transit Network Design Problem [0.7734726150561088]
  This work shows that single and multi objective stances can be synergistically combined to better answer the transit network design problem (TNDP) As a guiding case study, the solution is applied to the multimodal public transport network in the city of Lisbon, Portugal. The proposed TNDP optimization proved to improve results, with reductions in objective functions of up to 28.3%.
  arXiv  Detail & Related papers  (2022-01-27T16:22:07Z)
• Learnable Online Graph Representations for 3D Multi-Object Tracking [156.58876381318402]
  We propose a unified and learning based approach to the 3D MOT problem. We employ a Neural Message Passing network for data association that is fully trainable. We show the merit of the proposed approach on the publicly available nuScenes dataset by achieving state-of-the-art performance of 65.6% AMOTA and 58% fewer ID-switches.
  arXiv  Detail & Related papers  (2021-04-23T17:59:28Z)
• Risk-Averse MPC via Visual-Inertial Input and Recurrent Networks for Online Collision Avoidance [95.86944752753564]
  We propose an online path planning architecture that extends the model predictive control (MPC) formulation to consider future location uncertainties. Our algorithm combines an object detection pipeline with a recurrent neural network (RNN) which infers the covariance of state estimates. The robustness of our methods is validated on complex quadruped robot dynamics and can be generally applied to most robotic platforms.
  arXiv  Detail & Related papers  (2020-07-28T07:34:30Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.