Vignette d'image

Cotofrei, Paul

Nom
Cotofrei, Paul
Affiliation principale
Fonction
Maître d'enseignement et de recherche
Email
paul.cotofrei@unine.ch
Identifiants
https://libra.unine.ch/handle/123456789/595
_
0000-0002-4103-5467

Résultat de la recherche

Filtres

2
11
Réinitialiser les filtres

Paramètres

Voici les éléments 1 - 2 sur 2
  • Publication
    Accès libre
    From Forest to Zoo: Great Ape Behavior Recognition with ChimpBehave
    (2025-06-23)
    Fuchs, Michael 
    ;
    Genty, Emilie 
    ;
    Jean-Marc Odobez
    ;
    Bangerter, Adrian 
    ;
    Zuberbühler, Klaus 
    ;
    Cotofrei, Paul 
    Abstract This paper addresses the significant challenge of recognizing behaviors in non-human primates, specifically focusing on chimpanzees. Automated behavior recognition is crucial for both conservation efforts and the advancement of behavioral research. However, it is often hindered by the labor-intensive process of manual video annotation. Despite the availability of large-scale animal behavior datasets, effectively applying machine learning models across varied environmental settings remains a critical challenge due to the variability in data collection contexts and the specificity of annotations. In this paper, we introduce ChimpBehave, a novel dataset comprising over 2 h and 20 min of video (approximately 215,000 frames) of zoo-housed chimpanzees, annotated with bounding boxes and fine-grained locomotive behavior labels. Uniquely, ChimpBehave aligns its behavior classes with those in PanAf, an existing dataset collected in distinct visual environments, enabling the study of cross-dataset generalization - where models are trained on one dataset and tested on another with differing data distributions. We benchmark ChimpBehave using state-of-the-art video-based and skeleton-based action recognition models, establishing performance baselines for both within-dataset and cross-dataset evaluations. Our results highlight the strengths and limitations of different model architectures, providing insights into the application of automated behavior recognition across diverse visual settings. The dataset, models, and code can be accessed at: https://github.com/MitchFuchs/ChimpBehave
  • Publication
    Accès libre
    From Forest to Zoo: Domain Adaptation in Animal Behavior Recognition for Great Apes with ChimpBehave
    (2024-06-17)
    Fuchs, Michael 
    ;
    Genty, Emilie 
    ;
    Bangerter, Adrian 
    ;
    Zuberbühler, Klaus 
    ;
    Cotofrei, Paul 
    This paper addresses the significant challenge of recognizing behaviors in non-human primates, specifically focusing on chimpanzees. Automated behavior recognition is crucial for both conservation efforts and the advancement of behavioral research. However, it is significantly hindered by the labor-intensive process of manual video annotation. Despite the availability of large-scale animal behavior datasets, the effective application of machine learning models across varied environmental settings poses a critical challenge, primarily due to the variability in data collection contexts and the specificity of annotations. In this paper, we introduce ChimpBehave, a novel dataset featuring over 2 hours of video (approximately 193,000 video frames) of zoo-housed chimpanzees, meticulously annotated with bounding boxes and behavior labels for action recognition. ChimpBehave uniquely aligns its behavior classes with existing datasets, allowing for the study of domain adaptation and cross-dataset generalization methods between different visual settings. Furthermore, we benchmark our dataset using a state-of- theart CNN-based action recognition model, providing the first baseline results for both within and cross-dataset settings. The dataset, models, and code can be accessed at: https://github.com/MitchFuchs/ChimpBehave