Search Results for author: Peyman Moghadam

Found 16 papers, 7 papers with code

What's in the Black Box? The False Negative Mechanisms Inside Object Detectors

no code implementations15 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.

Object Detection Translation

InCloud: Incremental Learning for Point Cloud Place Recognition

no code implementations2 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.

Incremental Learning

Point Cloud Segmentation Using Sparse Temporal Local Attention

no code implementations1 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.

Autonomous Vehicles Frame +1

The CSIRO Crown-of-Thorn Starfish Detection Dataset

no code implementations29 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.

Dense Uncertainty Estimation

1 code implementation13 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.

Decision Making

LoGG3D-Net: Locally Guided Global Descriptor Learning for 3D Place Recognition

1 code implementation17 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.

Simultaneous Localization and Mapping

DeepSeagrass Dataset

1 code implementation9 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.

Multi-species Seagrass Detection and Classification from Underwater Images

1 code implementation18 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.

Classification General Classification

Temporally Coherent Embeddings for Self-Supervised Video Representation Learning

1 code implementation21 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.

Metric Learning Representation Learning +2

Spatiotemporal Camera-LiDAR Calibration: A Targetless and Structureless Approach

no code implementations17 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.

Skeleton Driven Non-rigid Motion Tracking and 3D Reconstruction

no code implementations9 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.

3D Reconstruction Frame

Deep Leaf Segmentation Using Synthetic Data

3 code implementations28 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).

Instance Segmentation Semantic Segmentation

Non-rigid Reconstruction with a Single Moving RGB-D Camera

no code implementations29 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.

Frame

Elastic LiDAR Fusion: Dense Map-Centric Continuous-Time SLAM

no code implementations6 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

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