Thermal Detection of People with Mobility Restrictions for Barrier Reduction at Traffic Lights Controlled Intersections

• URL: http://arxiv.org/abs/2505.08568v2
• Date: Wed, 14 May 2025 13:01:48 GMT
• Title: Thermal Detection of People with Mobility Restrictions for Barrier Reduction at Traffic Lights Controlled Intersections
• Authors: Xiao Ni, Carsten Kuehnel, Xiaoyi Jiang,
• Abstract summary: Rapid advances in deep learning for computer vision have driven the adoption of RGB camera-based adaptive traffic light systems.<n>Use of RGB cameras presents significant challenges, including limited detection performance under adverse weather or low-visibility conditions.<n>We propose a fully automated, thermal detector-based traffic light system that dynamically adjusts signal durations for individuals with walking impairments or mobility burden.
• Score: 4.27128470319992
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Rapid advances in deep learning for computer vision have driven the adoption of RGB camera-based adaptive traffic light systems to improve traffic safety and pedestrian comfort. However, these systems often overlook the needs of people with mobility restrictions. Moreover, the use of RGB cameras presents significant challenges, including limited detection performance under adverse weather or low-visibility conditions, as well as heightened privacy concerns. To address these issues, we propose a fully automated, thermal detector-based traffic light system that dynamically adjusts signal durations for individuals with walking impairments or mobility burden and triggers the auditory signal for visually impaired individuals, thereby advancing towards barrier-free intersection for all users. To this end, we build the thermal dataset for people with mobility restrictions (TD4PWMR), designed to capture diverse pedestrian scenarios, particularly focusing on individuals with mobility aids or mobility burden under varying environmental conditions, such as different lighting, weather, and crowded urban settings. While thermal imaging offers advantages in terms of privacy and robustness to adverse conditions, it also introduces inherent hurdles for object detection due to its lack of color and fine texture details and generally lower resolution of thermal images. To overcome these limitations, we develop YOLO-Thermal, a novel variant of the YOLO architecture that integrates advanced feature extraction and attention mechanisms for enhanced detection accuracy and robustness in thermal imaging. Experiments demonstrate that the proposed thermal detector outperforms existing detectors, while the proposed traffic light system effectively enhances barrier-free intersection. The source codes and dataset are available at https://github.com/leon2014dresden/YOLO-THERMAL.

Related papers

• SEPose: A Synthetic Event-based Human Pose Estimation Dataset for Pedestrian Monitoring [20.537672896807063]
  SEPose is a synthetic multi-person pose estimation dataset for fixed pedestrian perception generated using dynamic vision sensors in the CARLA simulator.<n>We train existing state-of-the-art models such as RVT and YOLOv8 on our dataset and evaluate them on real event-based data to demonstrate the sim-to-real generalization capabilities of the proposed dataset.
  arXiv  Detail & Related papers  (2025-07-16T04:54:11Z)
• A Novel Tuning Method for Real-time Multiple-Object Tracking Utilizing Thermal Sensor with Complexity Motion Pattern [7.6016974897939535]
  Multi-Object Tracking in thermal images is essential for surveillance systems.<n>The paper introduces a novel tuning method for pedestrian tracking, specifically designed to handle the complex motion patterns in thermal imagery.
  arXiv  Detail & Related papers  (2025-07-03T08:03:35Z)
• Distance Estimation in Outdoor Driving Environments Using Phase-only Correlation Method with Event Cameras [5.690128924544198]
  We present a method for distance estimation using a monocular event camera and a roadside LED bar.<n>The proposed approach achieves over 90% success rate with less than 0.5-meter error for distances ranging from 20 to 60 meters.<n>Future work includes extending this method to full position estimation by leveraging infrastructure such as smart poles equipped with LEDs.
  arXiv  Detail & Related papers  (2025-05-23T07:44:33Z)
• Leveraging Functional Encryption and Deep Learning for Privacy-Preserving Traffic Forecasting [3.177180500119784]
  We propose a secure, privacy-preserving location reporting and traffic forecasting system.<n>Our novel k-anonymity scheme utilize functional encryption to aggregate encrypted location information submitted by drivers.<n>We demonstrate the effectiveness of the proposed scheme with less than 10% mean absolute error for a 60-minute forecasting horizon.
  arXiv  Detail & Related papers  (2025-04-17T18:21:55Z)
• Multi-modal Multi-platform Person Re-Identification: Benchmark and Method [58.59888754340054]
  MP-ReID is a novel dataset designed specifically for multi-modality and multi-platform ReID.<n>This benchmark compiles data from 1,930 identities across diverse modalities, including RGB, infrared, and thermal imaging.<n>We introduce Uni-Prompt ReID, a framework with specific-designed prompts, tailored for cross-modality and cross-platform scenarios.
  arXiv  Detail & Related papers  (2025-03-21T12:27:49Z)
• Real-Time Multi-Scene Visibility Enhancement for Promoting Navigational Safety of Vessels Under Complex Weather Conditions [48.529493393948435]
  The visible-light camera has emerged as an essential imaging sensor for marine surface vessels in intelligent waterborne transportation systems. The visual imaging quality inevitably suffers from several kinds of degradations under complex weather conditions. We develop a general-purpose multi-scene visibility enhancement method to restore degraded images captured under different weather conditions.
  arXiv  Detail & Related papers  (2024-09-02T23:46:27Z)
• Detection of Micromobility Vehicles in Urban Traffic Videos [7.5867752610196915]
  This work introduces an adapted detection model that combines the accuracy and speed of single-frame object detection with the richer features offered by object detection frameworks. This fusion brings a temporal perspective to YOLOX detection abilities, allowing for a better understanding of urban mobility patterns. Tested on a curated dataset for urban micromobility scenarios, our model showcases substantial improvement over existing state-of-the-art methods.
  arXiv  Detail & Related papers  (2024-02-28T17:31:39Z)
• Camera-Radar Perception for Autonomous Vehicles and ADAS: Concepts, Datasets and Metrics [77.34726150561087]
  This work aims to carry out a study on the current scenario of camera and radar-based perception for ADAS and autonomous vehicles. Concepts and characteristics related to both sensors, as well as to their fusion, are presented. We give an overview of the Deep Learning-based detection and segmentation tasks, and the main datasets, metrics, challenges, and open questions in vehicle perception.
  arXiv  Detail & Related papers  (2023-03-08T00:48:32Z)
• Vision in adverse weather: Augmentation using CycleGANs with various object detectors for robust perception in autonomous racing [70.16043883381677]
  In autonomous racing, the weather can change abruptly, causing significant degradation in perception, resulting in ineffective manoeuvres. In order to improve detection in adverse weather, deep-learning-based models typically require extensive datasets captured in such conditions. We introduce an approach of using synthesised adverse condition datasets in autonomous racing (generated using CycleGAN) to improve the performance of four out of five state-of-the-art detectors.
  arXiv  Detail & Related papers  (2022-01-10T10:02:40Z)
• Does Thermal data make the detection systems more reliable? [1.2891210250935146]
  We propose a comprehensive detection system based on a multimodal-collaborative framework. This framework learns from both RGB (from visual cameras) and thermal (from Infrared cameras) data. Our empirical results show that while the improvement in accuracy is nominal, the value lies in challenging and extremely difficult edge cases.
  arXiv  Detail & Related papers  (2021-11-09T15:04:34Z)
• Exploring Thermal Images for Object Detection in Underexposure Regions for Autonomous Driving [67.69430435482127]
  Underexposure regions are vital to construct a complete perception of the surroundings for safe autonomous driving. The availability of thermal cameras has provided an essential alternate to explore regions where other optical sensors lack in capturing interpretable signals. This work proposes a domain adaptation framework which employs a style transfer technique for transfer learning from visible spectrum images to thermal images.
  arXiv  Detail & Related papers  (2020-06-01T09:59:09Z)
• Drone-based RGB-Infrared Cross-Modality Vehicle Detection via Uncertainty-Aware Learning [59.19469551774703]
  Drone-based vehicle detection aims at finding the vehicle locations and categories in an aerial image. We construct a large-scale drone-based RGB-Infrared vehicle detection dataset, termed DroneVehicle. Our DroneVehicle collects 28, 439 RGB-Infrared image pairs, covering urban roads, residential areas, parking lots, and other scenarios from day to night.
  arXiv  Detail & Related papers  (2020-03-05T05:29:44Z)

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.