Human Object Interaction Recognition Using Rate-Invariant Shape Analysis of Inter Joint Distances Trajectories

Meng Meng, hassen Drira, Mohamed Daoudi, Jacques Boonaert; Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, 2016, pp. 37-42

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 joint-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 and a shape analysis framework is used to model and compares 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.