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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

Action recognition and understanding through motor primitives

TL;DR: This work deals with single arm/hand actions which are very similar to each other in terms of arm/ hand motions, and uses a combination of discriminative support vector machines and generative hidden Markov models to model the process.
Journal ArticleDOI

A Bayesian Framework for Extracting Human Gait Using Strong Prior Knowledge

TL;DR: A consistent Bayesian framework for introducing strong prior knowledge into a system for extracting human gait is proposed, built from a simple articulated model having both time-invariant (static) and time-variant (dynamic) parameters.
Patent

Updating image segmentation following user input

TL;DR: In this article, the properties used in computing the different portions of the image are updated as a result of one or more user inputs, and an updated segmentation is post-processed such that only regions which are connected to an appropriate user input are updated.
Journal ArticleDOI

Adaptive Real-Time Emotion Recognition from Body Movements

TL;DR: A real-time system that continuously recognizes emotions from body movements is proposed, and a novel semisupervised adaptive algorithm is built on top of the conventional Random Forests classifier.
Journal ArticleDOI

Parallel Interacting Multiple Model-Based Human Motion Prediction for Motion Planning of Companion Robots

TL;DR: An autonomous motion planning framework for companion robots to accompany humans in a socially desirable manner, which takes safety and comfort requirements into account is proposed, and a nonlinear model predictive control technique is utilized for the robot motion planning.
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

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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

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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