Local and Global Contextual Features Fusion for Pedestrian Intention Prediction

M. Azarmi, M. Rezaei, T. Hussain, C. Qian

Research output: Chapter in Book/Report/Conference proceedingConference proceeding (ISBN)peer-review


Autonomous vehicles (AVs) are becoming an indispensable part of future transportation. However, safety challenges and lack of reliability limit their real-world deployment. Towards boosting the appearance of AVs on the roads, the interaction of AVs with pedestrians including “prediction of the pedestrian crossing intention” deserves extensive research. This is a highly challenging task as involves multiple non-linear parameters. In this direction, we extract and analyse spatio-temporal visual features of both pedestrian and traffic contexts. The pedestrian features include body pose and local context features that represent the pedestrian’s behaviour. Additionally, to understand the global context, we utilise location, motion, and environmental information using scene parsing technology that represents the pedestrian’s surroundings, and may affect the pedestrian’s intention. Finally, these multi-modality features are intelligently fused for effective intention prediction learning. The experimental results of the proposed model on the JAAD dataset show a superior result on the combined AUC and F1-score compared to the state-of-the-art.
Original languageEnglish
Title of host publicationArtificial Intelligence and Smart Vehicles
Subtitle of host publicationFirst International Conference, ICAISV 2023
EditorsMehdi Ghatee, S. Mehdi Hashemi
Number of pages13
ISBN (Electronic)9783031437632
ISBN (Print)9783031437625
Publication statusPublished - 5 Oct 2023
EventICAISV 2023 : International Conference on Artificial Intelligence and Smart Vehicles - Amirkabir University of Technology, Tehran, Iran, Islamic Republic of
Duration: 24 May 202425 May 2024

Publication series

NameCommunications in Computer and Information Science


ConferenceICAISV 2023
Country/TerritoryIran, Islamic Republic of


  • Pedestrian Crossing Intention
  • Pose Estimation
  • Semantic Segmentation
  • Pedestrian Intent Prediction
  • Autonomous Vehicles
  • Computer Vision
  • Human Action Prediction


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