Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
10281781 | Advanced Engineering Informatics | 2013 | 12 Pages |
Abstract
- We present a computer vision based method for equipment action recognition.
- Our vision-based method is based on a multiple binary SVM classifier and spatio-temporal features.
- A comprehensive real-world video dataset of excavator and truck actions is presented.
- We achieve accuracies of 86.33% and 98.33% for excavator and truck action classes.
- The presented method can be used for construction activity analysis using long sequences of videos.
Keywords
Related Topics
Physical Sciences and Engineering
Computer Science
Artificial Intelligence
Authors
Mani Golparvar-Fard, Arsalan Heydarian, Juan Carlos Niebles,