Search Results for author: Mohammud J. Bocus

Found 7 papers, 2 papers with code

Multimodal sensor fusion in the latent representation space

no code implementations3 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.

Denoising

OPERAnet: A Multimodal Activity Recognition Dataset Acquired from Radio Frequency and Vision-based Sensors

1 code implementation8 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.

Human Activity Recognition Multimodal Activity Recognition

Self-Supervised WiFi-Based Activity Recognition

no code implementations19 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.

Activity Recognition Contrastive Learning +1

ATG-PVD: Ticketing Parking Violations on A Drone

no code implementations21 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).

Optical Flow Estimation

We Learn Better Road Pothole Detection: from Attention Aggregation to Adversarial Domain Adaptation

1 code implementation16 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.

Domain Adaptation Semantic Segmentation

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