Cross-Modal Attention for Accurate Pedestrian Trajectory Prediction

Zaier, Mayssa; Wannous, Hazem; Drira, Hassen; Boonaert, Jacques

Type de document :

Communication dans un congrès avec actes

Titre :

Cross-Modal Attention for Accurate Pedestrian Trajectory Prediction

Auteur(s) :

Zaier, Mayssa [Auteur]
Centre for Digital Systems [CERI SN - IMT Nord Europe]
Wannous, Hazem [Auteur]

Centre de Recherche en Informatique, Signal et Automatique de Lille - UMR 9189 [CRIStAL]
Centre for Digital Systems [CERI SN - IMT Nord Europe]
Drira, Hassen [Auteur]

Laboratoire des sciences de l'ingénieur, de l'informatique et de l'imagerie [ICube]
Boonaert, Jacques [Auteur]
Centre for Digital Systems [CERI SN - IMT Nord Europe]

Titre de la manifestation scientifique :

34th British Machine Vision Conference 2023

Ville :

Aberdeen

Pays :

Royaume-Uni

Date de début de la manifestation scientifique :

2023-11-20

Discipline(s) HAL :

Informatique [cs]

Résumé en anglais : [en]

Accurately predicting human behavior is essential for a variety of applications, including self-driving cars, surveillance systems, and social robots. However, predicting human movement is challenging due to the complexity ...
Lire la suite >Accurately predicting human behavior is essential for a variety of applications, including self-driving cars, surveillance systems, and social robots. However, predicting human movement is challenging due to the complexity of physical environments and social interactions. Most studies focus on static environmental information, while ignoring the dynamic visual information available in the scene. To address this issue, we propose a novel approach called Cross-Modal Attention Trajectory Prediction (CMATP) able to predict human paths based on observed trajectory and dynamic scene context. Our approach uses a bimodal transformer network to capture complex spatio-temporal interactions and incorporates both pedestrian trajectory data and contextual information. Our approach achieves state-of-the-art performance on three real-world pedestrian prediction datasets, making it a promising solution for improving the safety and reliability of pedestrian detection and tracking systems. The code to reproduce our results is available at this link.Lire moins >

Langue :

Anglais

Comité de lecture :

Oui

Audience :

Internationale

Vulgarisation :

Non

Collections :

Centre de Recherche en Informatique, Signal et Automatique de Lille (CRIStAL) - UMR 9189

Source :

Harvested from HAL

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