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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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Ontology and taxonomy collaborated framework for meeting classification

TL;DR: This framework is novel and scalable, capable of adding new meeting types with no re-training, and has applications in automated video surveillance, video segmentation and retrieval (multimedia), human computer interaction, and augmented reality.
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Recognizing human action from a far field of view

TL;DR: A novel descriptor to characterize human action when it is being observed from a far field of view is presented, able to achieve perfect accuracy on two of the datasets, and perform comparably to other methods on the third dataset.
Proceedings ArticleDOI

Learning navigational maps by observing human motion patterns

TL;DR: This paper presents a methodology to allow a robot to navigate in a complex environment by observing pedestrian positional traces using a continuous probabilistic function determined using Gaussian process learning and used to infer the direction a robot should take in different parts of the environment.
Journal ArticleDOI

Single/multi-view human action recognition via regularized multi-task learning

TL;DR: The pyramid partwise bag of words (PPBoW) representation is proposed which implicitly encodes both local visual characteristics and human body structure and can achieve competing performance against the state-of-the-art methods for human action recognition on KTH and MV-TJU.
Posted Content

Long-Term Human Motion Prediction by Modeling Motion Context and Enhancing Motion Dynamic

TL;DR: In this article, a modified highway unit (MHU) is proposed for efficiently eliminating motionless joints and estimating next pose given the motion context, which improves the motion dynamic by minimizing the gram matrix loss for long-term motion prediction.
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

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Journal ArticleDOI

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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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