Abstract
Hazardous accidents often happen in construction sites and bring fatal consequences, and therefore safety management has been a certain dilemma to construction managers for long time. Although computer vision technology has been used on construction sites to identify construction workers and track their movement trajectories for safety management, the detection effect is often influenced by limited coverage of single cameras and occlusion. A multi-angle fusion method applying SURF feature algorithm is proposed to coalesce the information processed by improved GMM (Gaussian Mixed Model) and HOG + SVM (Histogram of Oriented Gradient and Support Vector Machines), identifying the obscured workers and achieving a better detection effect with larger coverage. Workers are tracked in real-time, with their movement trajectory estimated by utilizing Kalman filters and safety status analyzed to offer a prior warning signal. Experimental studies are conducted for validation of the proposed framework for workers’ detection and trajectories estimation, whose result indicates that the framework is able to detect workers and predict their movement trajectories for safety forewarning.
Highlights
To overcome the knowledge gaps, this paper proposes an improved method of worker detection with multi-angle information fusion and realizes the prediction of movement trajectory to determine their safety status and offer a prior warning signal, contributing to the on-site safety management
By taking Formula (13) as the state equation of the discrete time linear stochastic dynamic system described by Formula (17), the movement trajectory of construction workers can be predicted according to Formula (20), and the error of the predicted position is Formula (21)
After reviewing the literature referring to the safety management of on-site workers, this paper proposed a method to make up for the knowledge gap on multi-angle detection of workers and implemented a pre-warning method for construction workers’ dangerous status
Summary
This paper paper introduces introduces an an automated automated method methodfor for the the detection detection of of on-site on-site workers workers from from multiple multiple angles angles and and the the prediction prediction of of movement movement trajectory. This section section is is organized organized as as follows: improvement idea utilized in this paper, follows: (1). (1) introduction introductionofofthe themethods methodsand andsome some improvement idea utilized in this pasuch as improved. SVM framework for the detection (2) the improved GMM and HOG + SVM framework for the detection of of construction construction workers; workers; (3). (3) the the determination determination of of the the safety safety status status of of construction construction workers workers based based on on path path prediction; prediction; (4). (4) discussion, discussion, conclusion, conclusion, insufficient insufficient and and future futureexpectation
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