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Communication Dans Un Congrès Année : 2016

Human object interaction recognition using rate-invariant shape analysis of inter joint distances trajectories

Résumé

Human action recognition has emerged as one of the most challenging and active areas of research in the computer vision domain. In addition to pose variation and scale variability, high complexity of human motions and the variability of object interactions represent additional significant challenges. In this paper, we present an approach for human-object interaction modeling and classification. Towards that goal, we adopt relevant frame-level features; the inter-joint distances and joints-object distances. These proposed features are efficiently insensitive to position and pose variation. The evolution of the these distances in time is modeled by trajectories in a high dimension space and a shape analysis framework is used to model and compare the trajectories corresponding to human-object interaction in a Riemannian manifold. The experiments conducted following state-of-the-art settings and results demonstrate the strength of the proposed method. Using only the skeletal information , we achieve state-of-the-art classification results on the benchmark dataset.
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Dates et versions

hal-01703256 , version 1 (07-02-2018)

Identifiants

  • HAL Id : hal-01703256 , version 1

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Meng Meng, Hassen Drira, Mohamed Daoudi, Jacques Boonaert. Human object interaction recognition using rate-invariant shape analysis of inter joint distances trajectories. DIFF-CVML Workshop, 2016, Las vegas, United States. ⟨hal-01703256⟩
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