A Visual Surveillance System to Observe Realistic Road User Behavior for Improved Pedestrian and Cyclist Safety at Crossroads

Research output: Chapter in Book/Report/Conference proceedingConference paperpeer-review


Pedestrians and cyclists suffer the most serious injuries in traffic accidents. Existing Pedestrian Protection Systems and Road Safety Systems rely on an ideal model of pedestrian behavior and do not consider that people tend to take shortcuts, appear at unexpected places or can be distracted on the road, for example, by using a smartphone or wearing headphones. Collecting and analyzing realistic road user behavior is a crucial component to improve pedestrian and cyclist safety. However, such real-world data is still missing. To address this, we propose a visual surveillance system with two perpendicular partially overlapping fields of view, combined with a fully automated deep learning-based pipeline to process and collect video observations, detect and extract road user trajectories in real-world coordinates and estimate human attributes, such as age, gender, smartphone usage, etc. We demonstrate our prototype by deploying it in two locations in a European city.
Original languageEnglish
Title of host publicationAVSS 2022 - 18th IEEE International Conference on Advanced Video and Signal-Based Surveillance
ISBN (Electronic)978-1-6654-6382-9
Publication statusPublished - 29 Nov 2022
Event2022 18th IEEE International Conference on Advanced Video and Signal Based Surveillance: AVSS 2022 - Universidad Autónoma de Madrid, Madrid, Spain
Duration: 29 Nov 20222 Dec 2022
Conference number: 18


Conference2022 18th IEEE International Conference on Advanced Video and Signal Based Surveillance
Abbreviated titleAVSS 2022
Internet address


  • Pedestrian Protection
  • Pedestrian Safety
  • Detection and Tracking
  • Human Attribute Recognition
  • Intelligent Surveillance System
  • Deep Learning
  • Projective Geometry
  • Trajectory
  • Datasets

ASJC Scopus subject areas

  • Information Systems and Management
  • Artificial Intelligence
  • Computer Vision and Pattern Recognition
  • Computer Science Applications
  • Media Technology

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