Learning and classifying actions of construction workers and equipment using Bag-of-Video-Feature-Words and Bayesian network models

Automated action classification of construction workers and equipment from videos is a challenging problem that has a wide range of potential applications in construction. These applications include, but are not limited to, enabling rapid construction operation analysis and ergonomic studies. This research explores the potential of an emerging action analysis framework, Bag-of-Video-Feature-Words, in learning and classifying worker and heavy equipment actions in challenging construction environments. We developed a test bed that integrates the Bag-of-Video-Feature-Words model with Bayesian learning methods for evaluating the performance of this action analysis approach and tuning the model parameters. Video data sets were created for experimental evaluations. For each video data set, a number of action models were learned from training video segments and applied to testing video segments. Compared to previous studies on construction worker and equipment action classification, this new approach can achieve good performance in recognizing multiple action categories while robustly coping with the issues of partial occlusion, view point, and scale changes.