Search Results for author:
Found 8 papers, 3 papers with code
Data-Driven Depth Map Refinement via Multi-Scale Sparse Representation
Depth maps captured by consumer-level depth cameras such as Kinect are usually degraded by noise, missing values, and quantization.
RGB-Guided Hyperspectral Image Upsampling
On the contrary, latest imaging sensors capture a RGB image with resolution of multiple times larger than a hyperspectral image.
IMUTube: Automatic Extraction of Virtual on-body Accelerometry from Video for Human Activity Recognition
The lack of large-scale, labeled data sets impedes progress in developing robust and generalized predictive models for on-body sensor-based human activity recognition (HAR).
Fine-grained Human Activity Recognition Using Virtual On-body Acceleration Data
In this work we first introduce a measure to quantitatively assess the subtlety of human movements that are underlying activities of interest--the motion subtlety index (MSI)--which captures local pixel movements and pose changes in the vicinity of target virtual sensor locations, and correlate it to the eventual activity recognition accuracy.
Generating Virtual On-body Accelerometer Data from Virtual Textual Descriptions for Human Activity Recognition
We benchmarked our approach on three HAR datasets (RealWorld, PAMAP2, and USC-HAD) and demonstrate that the use of virtual IMU training data generated using our new approach leads to significantly improved HAR model performance compared to only using real IMU data.
A Feasibility Study on Indoor Localization and Multi-person Tracking Using Sparsely Distributed Camera Network with Edge Computing
To this end, we deployed an end-to-end edge computing pipeline that utilizes multiple cameras to achieve localization, body orientation estimation and tracking of multiple individuals within a large therapeutic space spanning $1700m^2$, all while maintaining a strong focus on preserving privacy.
On the Benefit of Generative Foundation Models for Human Activity Recognition
In human activity recognition (HAR), the limited availability of annotated data presents a significant challenge.
IMUGPT 2.0: Language-Based Cross Modality Transfer for Sensor-Based Human Activity Recognition
With the emergence of generative AI models such as large language models (LLMs) and text-driven motion synthesis models, language has become a promising source data modality as well as shown in proof of concepts such as IMUGPT.
