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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

Understanding human interactions with track and body synergies (TBS) captured from multiple views

TL;DR: A hypothesis-verification paradigm for top-down feedback that exploits the spatio-temporal constraints inherent in human interaction is presented and an experimental evaluation shows the efficacy of the proposed system for analyzing multi-person interactions.
Dissertation

Fitting Parametric Curve Models to Images Using Local Self-adapting Separation Criteria

Robert Hanek
TL;DR: The Contracting Curve Density algorithm is proposed as a solution to the curve-fitting problem and the use of blurred curve models enable the algorithm to trade-off two conflicting objectives, namely heaving a large area of convergence and achieving high accuracy.
Journal ArticleDOI

Dimensionality reduction using a Gaussian Process Annealed Particle Filter for tracking and classification of articulated body motions

TL;DR: A body pose tracker that uses the learned mapping function from latent space to body pose space and is shown to be robust when classifying individual actions and is also capable of the harder task of classifying interactions between people.
Journal ArticleDOI

Human action recognition based on semi-supervised discriminant analysis with global constraint

TL;DR: A novel algorithm named semi-supervised discriminant analysis with global constraint (SDG) is proposed which can better estimate the data distribution with both insufficient labeled data and sufficient unlabeled data.
Book ChapterDOI

Human Action Recognition Without Human

TL;DR: This paper considered whether a background sequence alone can classify human actions in current large-scale action datasets (e.g., UCF101) and concluded that some features from the background could be too strong.
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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