Proceedings ArticleDOI
Human motion analysis: a review
Jake K. Aggarwal,Qin Cai +1 more
- pp 90-102
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TLDR
The paper gives an overview of the various tasks involved in motion analysis of the human body, and focuses on three major areas related to interpreting human motion: motion analysis involving human body parts, tracking of human motion using single or multiple cameras, and recognizing human activities from image sequences.Abstract:
Human motion analysis is receiving increasing attention from computer vision researchers. This interest is motivated by a wide spectrum of applications, such as athletic performance analysis, surveillance, man-machine interfaces, content-based image storage and retrieval, and video conferencing. The paper gives an overview of the various tasks involved in motion analysis of the human body. The authors focus on three major areas related to interpreting human motion: 1) motion analysis involving human body parts, 2) tracking of human motion using single or multiple cameras, and 3) recognizing human activities from image sequences. Motion analysis of human body parts involves the low-level segmentation of the human body into segments connected by joints, and recovers the 3D structure of the human body using its 2D projections over a sequence of images. Tracking human motion using a single or multiple camera focuses on higher-level processing, in which moving humans are tracked without identifying specific parts of the body structure. After successfully matching the moving human image from one frame to another in image sequences, understanding the human movements or activities comes naturally, which leads to a discussion of recognizing human activities. The review is illustrated by examples.read more
Citations
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Bayesian inference by reversible jump MCMC for clustering based on finite generalized inverted Dirichlet mixtures
Sami Bourouis,Faisal R. Al-Osaimi,Nizar Bouguila,Hassen Sallay,Fahd M. Aldosari,Mohamed Al Mashrgy +5 more
TL;DR: A reversible jump Markov Chain Monte Carlo sampler for GID mixtures is developed that is applied for simultaneous clustering and feature selection in the context of some challenging real-world applications concerning scene classification, action recognition, and video forgery detection.
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Human motion capture using scalable body models
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Proceedings ArticleDOI
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