Search Results for author:
Found 8 papers, 3 papers with code
Aligning Actions and Walking to LLM-Generated Textual Descriptions
For action recognition, we employ LLMs to generate textual descriptions of actions in the BABEL-60 dataset, facilitating the alignment of motion sequences with linguistic representations.
Gait Recognition from Highly Compressed Videos
We systematically evaluate the performance of our artifact correction model against a range of noisy surveillance data and demonstrate that our approach not only achieves improved pose estimation on low-quality surveillance footage, but also preserves the integrity of the pose estimation on high resolution footage.
GaitFormer: Learning Gait Representations with Noisy Multi-Task Learning
Gait analysis is proven to be a reliable way to perform person identification without relying on subject cooperation.
Learning to Simplify Spatial-Temporal Graphs in Gait Analysis
However, these methods often rely on hand-crafted spatial-temporal graphs that are based on human anatomy disregarding the particularities of the dataset and task.
PsyMo: A Dataset for Estimating Self-Reported Psychological Traits from Gait
In this work, we propose PsyMo (Psychological traits from Motion), a novel, multi-purpose and multi-modal dataset for exploring psychological cues manifested in walking patterns.
GaitPT: Skeletons Are All You Need For Gait Recognition
Our results show that GaitPT achieves state-of-the-art performance compared to other skeleton-based gait recognition works, in both controlled and in-the-wild scenarios.
GaitMorph: Transforming Gait by Optimally Transporting Discrete Codes
Gait, the manner of walking, has been proven to be a reliable biometric with uses in surveillance, marketing and security.
WildGait: Learning Gait Representations from Raw Surveillance Streams
Existing methods for gait recognition require cooperative gait scenarios, in which a single person is walking multiple times in a straight line in front of a camera.
