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Histogram of Oriented Depth Gradients for Action Recognition

Nachwa Abou Bakr 1 James L. Crowley 1
1 PERVASIVE - Interaction située avec les objets et environnements intelligents
Inria Grenoble - Rhône-Alpes, LIG - Laboratoire d'Informatique de Grenoble
Abstract : In this paper, we report on experiments with the use of local measures for depth motion for visual action recognition from MPEG encoded RGBD video sequences. We show that such measures can be combined with local space-time video descriptors for appearance to provide a computationally efficient method for recognition of actions. Fisher vectors are used for encoding and concatenating a depth descriptor with existing RGB local descriptors. We then employ a linear SVM for recognizing manipulation actions using such vectors. We evaluate the effectiveness of such measures by comparison to the state-of-the-art using two recent datasets for action recognition in kitchen environments.
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Submitted on : Monday, January 29, 2018 - 12:01:40 PM
Last modification on : Thursday, January 20, 2022 - 5:26:14 PM
Long-term archiving on: : Friday, May 25, 2018 - 9:39:08 AM


  • HAL Id : hal-01694733, version 1
  • ARXIV : 1801.09477



Nachwa Abou Bakr, James L. Crowley. Histogram of Oriented Depth Gradients for Action Recognition. ORASIS 2017, Jun 2017, Colleville-sur-Mer, France. pp.1-2. ⟨hal-01694733⟩



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