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Conference papers
Robust human action recognition system using Laban Movement Analysis
Abstract : Recognizing human actions from video sequences is an active research area in computer vision. This paper describes an effective
approach to generate compact and informative representations for action recognition. We design a new action feature descriptor
inspired from Laban Movement Analysis method. An efficient preprocessing step based on view invariant human motion is presented.
Our descriptor is applied in four known machine learning methods, Random Decision Forest, Multi-Layer Perceptron and
Multi-class Support Vector Machines (One-Against-One and One-Against-All). Our proposed approach has been evaluated on two
challenging benchmarks of action recognition, Microsoft Research Cambridge-12 (MSRC-12) and MSR-Action3D.We follow the
same experimental settings to make a direct comparison between the four classifiers and to show the robustness of our descriptor
vector. Experimental results demonstrate that our approach outperforms the state-of-the-art methods.
https://hal.archives-ouvertes.fr/hal-01629272
Contributor : Frédéric Davesne <>
Submitted on : Monday, November 6, 2017 - 11:32:54 AM
Last modification on : Tuesday, June 30, 2020 - 11:56:09 AM
Contributor : Frédéric Davesne <>
Submitted on : Monday, November 6, 2017 - 11:32:54 AM
Last modification on : Tuesday, June 30, 2020 - 11:56:09 AM