Vision-based action recognition of earthmoving equipment using spatio-temporal features and support vector machine classifiers

- 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.