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Bingfei Zhang and Zhenhua Zhu

Vision-Based Detection of Falls at Flat Level Surfaces

Abstract: Workers might experience fall accidents even when they are working at flat level surfaces. These accidents plus other types of fall accidents have been reported as one of the major causes for worker-related fatalities and injuries. Currently, it becomes common to set up video cameras to monitor working environments. The video cameras provide an alternative to detect fall accidents. The objective of this paper is to investigate the feasibility of detecting fall accidents of workers with video. The preliminary focus is put on the fall detection under one single monocular camera. A novel fall detection method is proposed. Under the method, workers in the videos captured by the video cameras are first detected and tracked. Their pose and shape related features are then extracted. Given a set of features, an artificial neural network (ANN) classifier is further trained to automatically determine whether a fall happens. The method has been tested and the detection precision and recall were used to evaluate the method. The test results with high detection precision and recall indicated the method effectiveness. Also, the lessons and findings from this research are expected to build a solid foundation to create a vision-based fall detection solution for safety engineers.

Keywords: Fall Detection, Video Processing, Computer Vision, Safety Management

DOI: https://doi.org/10.24928/JC3-2017/0198

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Series: jc3:2017 (browse)
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Guangchun Zhou, Yaqub M. Rafiq, Chengfei Sui and Lingyan Xie

A CA And ANN Technique Of Predicting Failure Load And Failure Pattern Of Laterally Loaded Masonry Panel

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Full text: content.pdf (768,071 bytes) (available to registered users only)

Series: w78:2006 (browse)
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L C M Tang, L Xia, D A Adkins, A Cheshmehzangi

Smart Building Information Modelling: The Use of ANN-COBie for HVAC Information Capture and Exchanged

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Series: w78:2013 (browse)
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Lars Weber, K.-P. Holz

ANN Representation Of Finite Element Method For Groundwater Flow Simulation

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Mao Zhi, Goh Bee Hua, Wang Shouqing, Ofori G

Forecasting construction industry-level total factor productivity growth using neural network modeling

Abstract: Total Factor Productivity (TFP) is widely recognised as a better indicator than Labour Productivity and Multi-Factor Productivity to represent industry-level productivity performance. Productivity is the key determinant of a nation's standard of living and an industry's competitiveness. As such, the ability to predict trends in TFP growth in the construction industry is very important. The factors influencing TFP growth in the construction industry are complicatedly interrelated. This fact made the conventional regression method highly inadaptable to such complex multi-attribute nonlinear mappings. As an AI information-processing tool, the artificial neural network (ANN) system has been proven to be a powerful approach to solving complex nonlinear mappings with higher accuracy than regression methods. However, so far, there has been little application of ANNs in predicting TFP growth in the construction field. This study will for the first time, apply the concepts of ANNs to develop a model to forecast the TFP growth in the case of the construction industry of Singapore. Macro-level information processing models are useful in monitoring and predicting the performance of the construction industry as a whole. With the need to manage construction performance information at all three levels, namely, industry, firm and site, this study looks specifically at developing an 'intelligent' model for forecasting industry-level productivity. Meanwhile, using the same set of data, a model developed by the Multiple Linear Regression method will serve as a benchmark to judge the performance of the ANN model. The ANN model, compared with the traditional regression model, would be expected to have better forecasting ability for TFP growth in the construction industry, in terms of accuracy.

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Full text: content.pdf (79,002 bytes) (available to registered users only)

Series: w78:2002 (browse)
Cluster: papers of the same cluster (result of machine made clusters)
Class: class.analysis (0.037562) class.processing (0.012219) class.legal (0.002318)
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Permission to reproduce these documents have been graciously provided by the Aarhus School of Architecture, Denmark. The assistnace of the editor, Prof. Kristian Agger, is gratefully aprecciated.


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