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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

On the improvement of anthropometry and pose estimation from a single uncalibrated image

TL;DR: A generalization of that estimation algorithm that exploits pairwise geometric relationships of body segments to allow estimation from a broader class of images and the number of iterations needed during minimization are reduced tenfold.
Proceedings ArticleDOI

Detection of Fence Climbing from Monocular Video

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TL;DR: This paper presents a system that detects humans climbing fences by decoding the state sequence of the block based HMM, a block based discrete hidden Markov model (HMM) built with predefined action classes as the state blocks.
Patent

System architecture design for time-of-flight system having reduced differential pixel size, and time-of-flight systems so designed

TL;DR: In this article, the authors provide methods to produce a high performance, feature rich TOF system, phase-based or otherwise using small TOF pixels, single-ended or preferably differential, as well as TOF systems designed.
Patent

Dynamic camera based practice mode

TL;DR: In this article, a method for displaying a practice swing ball flight to a user in a virtual golf game is described, which illustrates to the user a ball flight resulting from a practiceswing, had the practice swing instead been an actual swing intended to strike a real golf ball.
Journal ArticleDOI

A light weight smartphone based human activity recognition system with high accuracy

TL;DR: Out-of-sample experimental results show that the proposed approach is able to recognize human activities from smartphones’ one-axis raw accelerometer sensor data, and achieves 100% accuracy for individual models across all activities and datasets.
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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