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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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Proceedings ArticleDOI

Tracking Human Hand Motion Using Genetic Particle Filter

TL;DR: This paper replaces the "Evolve" step of the particle filter by the mutation and crossover operators in the GA to solve the conventional Monte Carlo methods problems.
Journal ArticleDOI

Non-Rigid Structure-From-Motion With Uniqueness Constraint and Low Rank Matrix Fitting Factorization

TL;DR: A new constraint based framework for underconstrained non-rigid structure-from-motion problem to constrain the space of solution is proposed, based on a point trajectory approach with an additional uniqueness constraint applied to shape coefficients to reduce the basis required to construct the non- Rigid 3D shape.
Patent

Low inductance light source module

TL;DR: In this paper, a low inductance light source module comprising a connector having a layer of insulating material sandwiched between first and second conducting layers and a semiconducting light source was presented.

A Computer Vision System for Monitoring Production of Fast Food

TL;DR: Skin detection, shadow detection, and region-growing are used as low-level techniques in order to analyze a video of an employee preparing a hamburger or sandwich using a single static camera.
Book ChapterDOI

Recognizing Human Activities Using Non-linear SVM Decision Tree

TL;DR: A new method of human activity recognition based on \(\mathfrak{R}\) transform and non-linear SVM Decision Tree (NSVMDT), which is superior because the descriptor is robust to frame loss in activities recognition, simple representation, computational complexity and template generalization.
References
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Proceedings ArticleDOI

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Pfinder: real-time tracking of the human body

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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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