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Proceedings ArticleDOI

Human motion analysis: a review

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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.

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Citations
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Gait recognition using image self-similarity

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3D Articulated Models and Multiview Tracking with Physical Forces

TL;DR: A fast recursive algorithm is used to solve the dynamical equations of motion of any 3D articulated model using physical forces applied to each rigid part of a kinematic 3D model of the object the authors are tracking.
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Gesture personalization and profile roaming

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RGB-D-based Human Motion Recognition with Deep Learning: A Survey

TL;DR: A detailed overview of recent advances in RGB-D-based motion recognition is presented in this paper, where the reviewed methods are broadly categorized into four groups, depending on the modality adopted for recognition: RGB-based, depth based, skeleton-based and RGB+D based.
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Integration of Vision and Inertial Sensors for 3D Arm Motion Tracking in Home-based Rehabilitation

TL;DR: A real-time hybrid solution to articulated 3D arm motion tracking for home-based rehabilitation by combining visual and inertial sensors is introduced and compared with commercial marker-based systems, CODA and Qualysis.
References
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Proceedings ArticleDOI

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Proceedings ArticleDOI

Recognizing human action in time-sequential images using hidden Markov model

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Journal ArticleDOI

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TL;DR: The author uses projective relations as the theoretical foundation of his investigations of visual space and motion and concludes that during locomotion the components of the human visual environment are interpreted as rigid structures in relative motion.
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