SPDAGG-TransNet: Integrating Symmetric Positive Definite Networks with Transformers for UAV-Human Action Recognition - Université d'Évry
Communication Dans Un Congrès Année : 2024

SPDAGG-TransNet: Integrating Symmetric Positive Definite Networks with Transformers for UAV-Human Action Recognition

Résumé

The advent of unmanned aerial vehicles (UAVs) has initiated a revolutionary era in human action recognition, profoundly influencing various domains. This transition underscores the critical necessity for comprehensive benchmarks crucial for formulating and evaluating UAV-centric models tailored to human behavior analysis. This paper presents an novel approach called SPDAGG-TransNet network for UAV-human action recognition, leveraging the resilience of skeletal-based features amidst these obstacles. Our approach revolves around a deep neural network adept at capturing the intricate spatial and temporal dimensions of human actions, leading to the development of Semi-Positive Definite (SPD) matrix representations. These representations are then transformed using a transformer encoder before being classified using a Multilayer Perceptron (MLP). To assess the effectiveness of our approach, we conduct thorough evaluations using publicly available datasets such as the UAV-Human Action Recognition and UAV-Gesture datasets. Our findings underscore the state-of-the-art performance achieved by our method, highlighting its potential to significantly advance UAV-based human action recognition.
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Dates et versions

hal-04853190 , version 1 (21-12-2024)

Identifiants

Citer

Mohamed Sanim Akremi, Najett Neji, Hedi Tabia. SPDAGG-TransNet: Integrating Symmetric Positive Definite Networks with Transformers for UAV-Human Action Recognition. IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2024), Oct 2024, Abu Dhabi, United Arab Emirates. pp.4479-4486, ⟨10.1109/IROS58592.2024.10802016⟩. ⟨hal-04853190⟩
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