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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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Journal ArticleDOI

Advances in View-Invariant Human Motion Analysis: A Review

TL;DR: Recent development in three major issues involved in a general human motion analysis system, namely, human detection, view-invariant pose representation and estimation, and behavior understanding are presented.
Journal ArticleDOI

Hand modeling, analysis and recognition

TL;DR: 3-D hand models, various articulated motion analysis methods, and gesture recognition techniques employed in current research are studied, some of which are shown as examples.
Proceedings ArticleDOI

3D articulated models and multi-view tracking with silhouettes

Q. Delamarre, +1 more
TL;DR: A fast algorithm that computes the motion of the articulated 3D model of a person filmed by two or more fixed cameras based on the latest works on calibration and image segmentation developed in the lab is developed.
Journal ArticleDOI

Human detection from images and videos

TL;DR: A comprehensive survey on the recent development and challenges of human detection in the thread of human object descriptors is provided, providing a thorough analysis of the state-of-the-art human detection methods and a guide to the selection of appropriate methods in practical applications.
Journal ArticleDOI

Understanding Video Events: A Survey of Methods for Automatic Interpretation of Semantic Occurrences in Video

TL;DR: This survey discusses this proposed taxonomy of the literature, offers a unifying terminology, and discusses popular event modeling formalisms and their use in video event understanding using extensive examples from the literature.
References
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Proceedings ArticleDOI

Determining Optical Flow

TL;DR: In this article, a method for finding the optical flow pattern is presented which assumes that the apparent velocity of the brightness pattern varies smoothly almost everywhere in the image, and an iterative implementation is shown which successfully computes the Optical Flow for a number of synthetic image sequences.
Journal ArticleDOI

Pfinder: real-time tracking of the human body

TL;DR: Pfinder is a real-time system for tracking people and interpreting their behavior that uses a multiclass statistical model of color and shape to obtain a 2D representation of head and hands in a wide range of viewing conditions.
Journal ArticleDOI

Representation and recognition of the spatial organization of three-dimensional shapes.

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

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

Visual motion perception.

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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