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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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Dissertation

Estimation algorithms for ambiguous visual models : Three Dimensional Human Modeling and Motion Reconstruction in Monocular Video Sequences

TL;DR: A model is proposed that incorporates realistic kinematics and several important human body constraints, and a principled, robust and probabilistically motivated integration of different visual cues like contours, intensity or silhouettes, and three novel continuous multiple-hypothesis search techniques that allow either deterministic or stochastic localization of nearby peaks in the high-dimensional human pose likelihood surface.

Syntactic Modeling and Signal Processing of Multifunction Radars: A Stochastic Context-Free Grammar Approach Using improved pattern recognition techniques, the target of a radar system can model and identify that system and estimate how much of a threat it poses.

TL;DR: This paper demonstrates that stochastic context-free grammars (SCFGs) are adequate models for capturing the essential features of the MFR dynamics and derives two statistical estimation approaches for MFR signal processing-a maximum likelihood sequence estimator to estimate radar's policies of operation, and a maximum likelihood parameter estimators to infer the radar parameter values.
Journal ArticleDOI

Using Discrete Cosine Transform Based Features for Human Action Recognition

TL;DR: A discrete cosine transform based features have been exploited for action recognition and K-Nearest Neighbor (K-NN) classifier is used for classification.
Journal ArticleDOI

Recognizing Walking People

TL;DR: The method is virtually parameter free and computes a consistency residual between a pair of sequences that can be used as a distance for clustering and classification and presents experimental results that are superior to those of previously published algorithms both in terms of performance and generality.
Journal ArticleDOI

Robust human action recognition scheme based on high-level feature fusion

TL;DR: A generic approach applied to different periodic and non-periodic actions in the same framework defined by Weizmann and KTH datasets is investigated, and the notion of self-similarity descriptor over time is explored, to improve the classification rate by pushing classifiers into an optimized structure.
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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