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View Invariant Human Action Recognition Using Histograms



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View Invariant Human Action Recognition Using Histograms of 3D Joints Lu Xia Chia Chih Chen and J K Aggarwal Computer Vision Research Center Department of ECE The University of Texas at Austin xialu ccchen utexas edu aggarwaljk mail utexas edu Abstract In this paper we present a novel approach for human action recognition with histograms of 3D joint locations HOJ3D as a compact representation of postures We extract the 3D skeletal joint locations from Kinect depth maps using Shotton et al s method 6 The HOJ3D computed from the action depth sequences are reprojected using LDA and then clustered into k posture visual words which represent the prototypical poses of actions The temporal evolutions of those visual words are modeled by discrete hidden Markov models HMMs In addition due to the design of our spherical coordinate system and the robust 3D skeleton estimation from Kinect our method demonstrates significant view invariance on our 3D action dataset Our dataset is composed of 200 3D sequences of 10 indoor activities performed by 10 individuals in varied views Our method is real time and achieves superior results on the challenging 3D action dataset We also tested our algorithm on the MSR Action3D dataset and our algorithm outperforms Li et al 25 on most of the cases 1 Introduction Human action recognition is a widely studied area in computer vision Its applications include surveillance systems video analysis robotics and a variety of systems that involve interactions between persons and electronic devices such as human computer interfaces Its development began in the early 1980s To date research has mainly focused on learning and recognizing actions from video sequences taken by a single visible light camera There is extensive literature in action recognition in a number of fields including computer vision machine learning pattern recognition signal processing etc 1 2 Among the different types of features for representation silhouettes and spatio temporal



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