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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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Volumetric Features for Video Event Detection

TL;DR: This work proposes a technique for event recognition in crowded videos that reliably identifies actions in the presence of partial occlusion and background clutter, enabling robustness against occlusions and actor variability.
Patent

Bringing a visual representation to life via learned input from the user

TL;DR: In this paper, a capture device that captures data about the user in the physical space is used to identify the user's characteristics, tendencies, voice patterns, behaviors, gestures, etc.
Journal ArticleDOI

Fuzzy Qualitative Human Motion Analysis

TL;DR: A fuzzy qualitative approach to vision-based human motion analysis with an emphasis on human motion recognition is proposed by combining fuzzy qualitative robot kinematics with human motion tracking and recognition algorithms and consistently outperforms conventional hidden Markov model and fuzzy HMM.
Proceedings ArticleDOI

Markerless Motion Capture using Multiple Cameras

TL;DR: The ultimate objective is to build an end-to-end system that can integrate the above mentioned components into a completely automated markerless motion capture system.
Journal ArticleDOI

Gesture recognition using Bezier curves for visualization navigation from registered 3-D data

TL;DR: A geometric method using Bezier curves is used for the trajectory analysis and classification of gestures, and the hand gesture speed is incorporated into the algorithm to enable correct recognition from trajectories with variations in hand speed.
References
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Proceedings ArticleDOI

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TL;DR: The human visual process can be studied by examining the computational problems associated with deriving useful information from retinal images by applying the approach to the problem of representing three-dimensional shapes for the purpose of recognition.
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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