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Journal ArticleDOI

Comparative study of vision tracking methods for tracking of construction site resources

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TLDR
A comparative study of various vision tracker categories is carried out, to identify which one is most effective in tracking construction resources and the most suitable tracker needed to research effective 3D vision trackers of construction resources.
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This article is published in Automation in Construction.The article was published on 2011-11-01. It has received 114 citations till now. The article focuses on the topics: Eye tracking & BitTorrent tracker.

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Journal ArticleDOI

Computer vision techniques for construction safety and health monitoring

TL;DR: This paper categorizes previous studies into three groups-object detection, object tracking, and action recognition-based on types of information required to evaluate unsafe conditions and acts, and provides researchers insights into advancing knowledge and techniques for computer vision-based safety and health monitoring.
Journal ArticleDOI

A deep hybrid learning model to detect unsafe behavior: Integrating convolution neural networks and long short-term memory

TL;DR: The results reveal that the developed hybrid model (CNN + LSTM) is able to accurately detect safe/unsafe actions conducted by workers on-site and exceeds the current state-of-the-art descriptor-based methods for detecting points of interest on images.
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A vision-based motion capture and recognition framework for behavior-based safety management

TL;DR: A framework of vision-based unsafe action detection for behavior monitoring to provide a robust and automated means for worker observation and indicates that the proposed framework can potentially perform well at detecting predefined unsafe actions in videos.
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Construction worker detection in video frames for initializing vision trackers

TL;DR: The proposed method exploits motion, shape, and color cues to narrow down the detection regions to moving objects, people, and finally construction workers, respectively, and demonstrates its suitability for automatic initialization of vision trackers.
Journal ArticleDOI

Automated vision tracking of project related entities

TL;DR: A vision based tracking framework that holds promise to addressPrivacy issues with personnel tracking often limits the usability of these technologies on construction sites, and the results are presented to illustrate the feasibility of the framework.
References
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Proceedings Article

An iterative image registration technique with an application to stereo vision

TL;DR: In this paper, the spatial intensity gradient of the images is used to find a good match using a type of Newton-Raphson iteration, which can be generalized to handle rotation, scaling and shearing.
Proceedings ArticleDOI

Good features to track

TL;DR: A feature selection criterion that is optimal by construction because it is based on how the tracker works, and a feature monitoring method that can detect occlusions, disocclusions, and features that do not correspond to points in the world are proposed.
Journal ArticleDOI

C ONDENSATION —Conditional Density Propagation forVisual Tracking

TL;DR: The Condensation algorithm uses “factored sampling”, previously applied to the interpretation of static images, in which the probability distribution of possible interpretations is represented by a randomly generated set.
Journal ArticleDOI

Object tracking: A survey

TL;DR: The goal of this article is to review the state-of-the-art tracking methods, classify them into different categories, and identify new trends to discuss the important issues related to tracking including the use of appropriate image features, selection of motion models, and detection of objects.
Journal ArticleDOI

Kernel-based object tracking

TL;DR: A new approach toward target representation and localization, the central component in visual tracking of nonrigid objects, is proposed, which employs a metric derived from the Bhattacharyya coefficient as similarity measure, and uses the mean shift procedure to perform the optimization.
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