no code implementations • 20 May 2023 • Alex Iacob, Pedro P. B. Gusmão, Nicholas D. Lane, Armand K. Koupai, Mohammud J. Bocus, Raúl Santos-Rodríguez, Robert J. Piechocki, Ryan McConville
This work studies the impact of privacy in federated HAR at a user, environment, and sensor level.
no code implementations • 24 Feb 2023 • Mohammud J. Bocus, Xiaoyang Wang, Robert. J. Piechocki
This paper presents a novel approach for multimodal data fusion based on the Vector-Quantized Variational Autoencoder (VQVAE) architecture.
no code implementations • 15 Aug 2022 • Armand K. Koupai, Mohammud J. Bocus, Raul Santos-Rodriguez, Robert J. Piechocki, Ryan McConville
We first propose the Fusion Transformer, an attention-based model for multimodal and multi-sensor fusion.
no code implementations • 3 Aug 2022 • Robert J. Piechocki, Xiaoyang Wang, Mohammud J. Bocus
In the second stage, the generative model serves as a reconstruction prior and the search manifold for the sensor fusion tasks.
no code implementations • 24 Dec 2021 • Jiahe Fan, Mohammud J. Bocus, Brett Hosking, Rigen Wu, Yanan Liu, Sergey Vityazev, Rui Fan
This further reduces the semantic gap between different feature channel layers.
1 code implementation • 8 Oct 2021 • Mohammud J. Bocus, Wenda Li, Shelly Vishwakarma, Roget Kou, Chong Tang, Karl Woodbridge, Ian Craddock, Ryan McConville, Raul Santos-Rodriguez, Kevin Chetty, Robert Piechocki
This dataset can be exploited to advance WiFi and vision-based HAR, for example, using pattern recognition, skeletal representation, deep learning algorithms or other novel approaches to accurately recognize human activities.
no code implementations • 19 Apr 2021 • Hok-Shing Lau, Ryan McConville, Mohammud J. Bocus, Robert J. Piechocki, Raul Santos-Rodriguez
Traditional approaches to activity recognition involve the use of wearable sensors or cameras in order to recognise human activities.
no code implementations • 21 Aug 2020 • Hengli Wang, Yuxuan Liu, Huaiyang Huang, Yuheng Pan, Wenbin Yu, Jialin Jiang, Dianbin Lyu, Mohammud J. Bocus, Ming Liu, Ioannis Pitas, Rui Fan
In this paper, we introduce a novel suspect-and-investigate framework, which can be easily embedded in a drone for automated parking violation detection (PVD).
1 code implementation • 16 Aug 2020 • Rui Fan, Hengli Wang, Mohammud J. Bocus, Ming Liu
The experimental results demonstrate that, firstly, the transformed disparity (or inverse depth) images become more informative; secondly, AA-UNet and AA-RTFNet, our best performing implementations, respectively outperform all other state-of-the-art single-modal and data-fusion networks for road pothole detection; and finally, the training set augmentation technique based on adversarial domain adaptation not only improves the accuracy of the state-of-the-art semantic segmentation networks, but also accelerates their convergence.