Automatic excavator action recognition and localisation for untrimmed video using hybrid LSTM-Transformer networks
A.H. Martin, Andrew J. Hill, Konstantin M. Seiler, Mehala Balamurali
Abstract
In mining and construction, excavators are integral to earth-moving operations. Accurate knowledge of excavator activities may be used in productivity analysis to streamline delivery. This paper presents a computer vision-based method for excavator action detection which can automatically inference the occurrence and time duration of excavator actions from untrimmed video captured from the excavator cab. The model uses a three-stage architecture consisting of a VGG16 feature extractor, a four-stage Transformer Encoder-Long Short-Term Memory (LSTM) module, and a post-processing component. The model’s predictive performance has been validated on the largest dataset among similar studies, comprising 567,000 frames filmed on-site at day and night. When tested on night and daytime videos, the model achieves accuracies of 90% and 70%, respectively, highlighting strong potential for practical implementation of the Transformer-LSTM network in excavator action detection. This study presents the first application of the combined Transformer-LSTM network for action detection in computer vision.