All Outputs (5)

A debris clearance robot for extreme environments (2019)
Journal Article
West, C., Giuliani, M., Lennox, B., Cheah, W., Arvin, F., West, A., & Watson, S. (2019). A debris clearance robot for extreme environments. Lecture Notes in Artificial Intelligence, 11649 LNAI, 148-159. https://doi.org/10.1007/978-3-030-23807-0_13

© Springer Nature Switzerland AG 2019. The need for nuclear decommissioning is increasing globally, as power stations and other facilities utilising nuclear reaches the end of their operational life. Currently the majority of decommissioning tasks ar... Read More about A debris clearance robot for extreme environments.

Feature and performance comparison of the V-REP, Gazebo and ARGoS robot simulators (2018)
Journal Article
Pitonakova, L., Giuliani, M., Pipe, A., & Winfield, A. (2018). Feature and performance comparison of the V-REP, Gazebo and ARGoS robot simulators. Lecture Notes in Artificial Intelligence, 10965 LNAI, 357-368. https://doi.org/10.1007/978-3-319-96728-8_30

© Springer International Publishing AG, part of Springer Nature 2018. In this paper, the characteristics and performance of three open-source simulators for robotics, V-REP, Gazebo and ARGoS, are thoroughly analysed and compared. While they all allow... Read More about Feature and performance comparison of the V-REP, Gazebo and ARGoS robot simulators.

Multi-modality gesture detection and recognition with un-supervision, randomization and discrimination (2015)
Journal Article
Wu, D., Chen, G., Clarke, D., Weikersdorfer, D., Giuliani, M., Gaschler, A., & Knoll, A. (2015). Multi-modality gesture detection and recognition with un-supervision, randomization and discrimination. Lecture Notes in Artificial Intelligence, 8925, 608-622. https://doi.org/10.1007/978-3-319-16178-5_43

© Springer International Publishing Switzerland 2015. We describe in this paper our gesture detection and recognition system for the 2014 ChaLearn Looking at People (Track 3: Gesture Recognition) organized by ChaLearn in conjunction with the ECCV 201... Read More about Multi-modality gesture detection and recognition with un-supervision, randomization and discrimination.

Action recognition using ensemble weighted multi-instance learning (2014)
Journal Article
Chen, G., Giuliani, M., Clarke, D., Gaschler, A., & Knoll, A. (2014). Action recognition using ensemble weighted multi-instance learning. IEEE International Conference on Robotics and Automation, 4520-4525. https://doi.org/10.1109/ICRA.2014.6907519

© 2014 IEEE. This paper deals with recognizing human actions in depth video data. Current state-of-the-art action recognition methods use hand-designed features, which are difficult to produce and time-consuming to extend to new modalities. In this p... Read More about Action recognition using ensemble weighted multi-instance learning.

Unsupervised learning spatio-temporal features for human activity recognition from RGB-D video data (2013)
Journal Article
Chen, G., Zhang, F., Giuliani, M., Buckl, C., & Knoll, A. (2013). Unsupervised learning spatio-temporal features for human activity recognition from RGB-D video data. Lecture Notes in Artificial Intelligence, 8239 LNAI, 341-350. https://doi.org/10.1007/978-3-319-02675-6_34

Being able to recognize human activities is essential for several applications, including social robotics. The recently developed commodity depth sensors open up newpossibilities of dealingwith this problem. Existing techniques extract hand-tuned fea... Read More about Unsupervised learning spatio-temporal features for human activity recognition from RGB-D video data.