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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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Book ChapterDOI

aSpaces: Action Spaces for Recognition and Synthesis of Human Actions

TL;DR: A novel human action model is presented, called the aSpace, based on a Point Distribution Model (PDM), which is compact, accurate and specific.
Journal ArticleDOI

Matching Trajectories of Anatomical Landmarks Under Viewpoint, Anthropometric and Temporal Transforms

TL;DR: An approach is presented to match imaged trajectories of anatomical landmarks using semantic correspondences between human bodies using semantic corresponds to provide geometric constraints for matching actions observed from different viewpoints and performed at different rates by actors of differing anthropometric proportions.
Journal ArticleDOI

A Monocular Marker-Free Gait Measurement System

TL;DR: A new, user-friendly, portable motion capture and gait analysis system for capturing and analyzing human gait, designed as a telemedicine tool to monitor remotely the progress of patients through treatment.
Journal ArticleDOI

Boosted Exemplar Learning for Action Recognition and Annotation

TL;DR: A boosted exemplar learning (BEL) approach to model various actions in a weakly supervised manner, i.e., only action bag-level labels are provided but action instance level ones are not, and is applied to learn representations of actions by using images collected from the Web.
Book ChapterDOI

Advances in Video-Based Human Activity Analysis: Challenges and Approaches

TL;DR: This chapter shall review state-of-the-art computer vision algorithms that address the issues of interpretation and identification of human activities in video and then provide a unified perspective from which specific algorithms can be derived.
References
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Proceedings ArticleDOI

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Proceedings ArticleDOI

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TL;DR: The recognition rate is improved by increasing the number of people used to generate the training data, indicating the possibility of establishing a person-independent action recognizer.
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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