Publications

This paper presents a self-supervised approach for learning to associate object detections in a video sequence as often required in tracking-by-detection systems.
[ pdf | bib ]
A novel deep convolutional neural network (DCNN) architecture is proposed for fine-grained image classification. This architecture, called MixDCNN, combines the output of several DCNNs within a mixture model framework and is shown to outperform other methods.
[ arxiv | bib ]
A background modeling approach to reducing the false positive rate of a pre-trained object detector for use in an open-pit mining environment.
[ pdf | bib ]
This paper presents a novel method to improve fine-grained classification based on hierarchical subset learning. First a similarity tree is formed where classes with strong visual correlations are grouped into subsets. An expert local classifier with strong discriminative power to distinguish visually similar classes is then learnt for each subset.
[ pdf | bib ]
A training free method for detecting and tracking moving objects is presented and evaluated with video footage from a moving camera.
[ pdf | bib | video]
A simple, yet efficient method for finding nearest neighbours in projected 3D point clouds is presented with applications towards object segmentation.
[ pdf | bib ]
This paper presents a method for measuring the in-bucket payload volume on a dragline excavator for the purpose of estimating the material’s bulk density in real-time.
[ pdf | bib ]
This paper details the implementation and trialling of a prototype in-bucket bulk density monitor on a production dragline.
[ pdf | bib ]