no code implementations • 15 Mar 2022 • Dimity Miller, Peyman Moghadam, Mark Cox, Matt Wildie, Raja Jurdak
Using this framework, we investigate why Faster R-CNN and RetinaNet fail to detect objects in benchmark vision datasets and robotics datasets.
no code implementations • 2 Mar 2022 • Joshua Knights, Peyman Moghadam, Milad Ramezani, Sridha Sridharan, Clinton Fookes
In this paper we address the problem of incremental learning for point cloud place recognition and introduce InCloud, a structure-aware distillation-based approach which preserves the higher-order structure of the network's embedding space.
no code implementations • 1 Dec 2021 • Joshua Knights, Peyman Moghadam, Clinton Fookes, Sridha Sridharan
Point clouds are a key modality used for perception in autonomous vehicles, providing the means for a robust geometric understanding of the surrounding environment.
no code implementations • 29 Nov 2021 • Jiajun Liu, Brano Kusy, Ross Marchant, Brendan Do, Torsten Merz, Joey Crosswell, Andy Steven, Nic Heaney, Karl Von Richter, Lachlan Tychsen-Smith, David Ahmedt-Aristizabal, Mohammad Ali Armin, Geoffrey Carlin, Russ Babcock, Peyman Moghadam, Daniel Smith, Tim Davis, Kemal El Moujahid, Martin Wicke, Megha Malpani
Crown-of-Thorn Starfish (COTS) outbreaks are a major cause of coral loss on the Great Barrier Reef (GBR) and substantial surveillance and control programs are underway in an attempt to manage COTS populations to ecologically sustainable levels.
1 code implementation • 13 Oct 2021 • Jing Zhang, Yuchao Dai, Mochu Xiang, Deng-Ping Fan, Peyman Moghadam, Mingyi He, Christian Walder, Kaihao Zhang, Mehrtash Harandi, Nick Barnes
Deep neural networks can be roughly divided into deterministic neural networks and stochastic neural networks. The former is usually trained to achieve a mapping from input space to output space via maximum likelihood estimation for the weights, which leads to deterministic predictions during testing.
1 code implementation • 17 Sep 2021 • Kavisha Vidanapathirana, Milad Ramezani, Peyman Moghadam, Sridha Sridharan, Clinton Fookes
Experiments on two large-scale public benchmarks (KITTI and MulRan) show that our method achieves mean $F1_{max}$ scores of $0. 939$ and $0. 968$ on KITTI and MulRan respectively, achieving state-of-the-art performance while operating in near real-time.
1 code implementation • 9 Mar 2021 • Scarlett Raine, Ross Marchant, Peyman Moghadam, Frederic Maire, Brett Kettle, Brano Kusy
We introduce a dataset of seagrass images collected by a biologist snorkelling in Moreton Bay, Queensland, Australia, as described in our publication: arXiv:2009. 09924.
1 code implementation • 18 Sep 2020 • Scarlett Raine, Ross Marchant, Peyman Moghadam, Frederic Maire, Brett Kettle, Brano Kusy
Underwater surveys conducted using divers or robots equipped with customized camera payloads can generate a large number of images.
3 code implementations • Under review 2020 • Daniel Ward, Peyman Moghadam
This study is applicable to use of synthetic data for automating the measurement of phenotypic traits.
1 code implementation • 21 Mar 2020 • Joshua Knights, Ben Harwood, Daniel Ward, Anthony Vanderkop, Olivia Mackenzie-Ross, Peyman Moghadam
The proposed method exploits inherent structure of unlabeled video data to explicitly enforce temporal coherency in the embedding space, rather than indirectly learning it through ranking or predictive proxy tasks.
Ranked #29 on
Self-Supervised Action Recognition
on UCF101
no code implementations • 17 Jan 2020 • Chanoh Park, Peyman Moghadam, Soohwan Kim, Sridha Sridharan, Clinton Fookes
The demand for multimodal sensing systems for robotics is growing due to the increase in robustness, reliability and accuracy offered by these systems.
no code implementations • 7 Mar 2019 • Shafeeq Elanattil, Peyman Moghadam
We introduce a synthetic dataset for evaluating non-rigid 3D human reconstruction based on conventional RGB-D cameras.
no code implementations • 9 Oct 2018 • Shafeeq Elanattil, Peyman Moghadam, Simon Denman, Sridha Sridharan, Clinton Fookes
We propose a puppet model-based tracking approach using skeleton prior, which provides a better initialization for tracking articulated movements.
3 code implementations • 28 Jul 2018 • Daniel Ward, Peyman Moghadam, Nicolas Hudson
Our proposed approach achieves 90% leaf segmentation score on the A1 test set outperforming the-state-of-the-art approaches for the CVPPP Leaf Segmentation Challenge (LSC).
no code implementations • 29 May 2018 • Shafeeq Elanattil, Peyman Moghadam, Sridha Sridharan, Clinton Fookes, Mark Cox
Our approach uses camera pose estimated from the rigid background for foreground tracking.
no code implementations • 6 Nov 2017 • Chanoh Park, Peyman Moghadam, Soohwan Kim, Alberto Elfes, Clinton Fookes, Sridha Sridharan
The concept of continuous-time trajectory representation has brought increased accuracy and efficiency to multi-modal sensor fusion in modern SLAM.
Robotics