paper-with-me

Papers

mmPose-NLP: A Natural Language Processing Approach to Precise Skeletal Pose Estimation using mmWave Radars

2021-07-21 · Arindam Sengupta, Siyang Cao

In this paper we presented mmPose-NLP, a novel Natural Language Processing (NLP) inspired Sequence-to-Sequence (Seq2Seq) skeletal key-point estimator using millimeter-wave (mmWave) radar data. To the best of the author's knowledge, this is the first method to precisely estimate upto 25 skeletal key-points using mmWave radar data alone. Skeletal pose estimation is critical in several applications ranging from autonomous vehicles, traffic monitoring, patient monitoring, gait analysis, to defense security forensics, and aid both preventative and actionable decision making. The use of mmWave radars for this task, over traditionally employed optical sensors, provide several advantages, primarily its operational robustness to scene lighting and adverse weather conditions, where optical sensor performance degrade significantly. The mmWave radar point-cloud (PCL) data is first voxelized (analogous to tokenization in NLP) and $N$ frames of the voxelized radar data (analogous to a text paragraph in NLP) is subjected to the proposed mmPose-NLP architecture, where the voxel indices of the 25 skeletal key-points (analogous to keyword extraction in NLP) are predicted. The voxel indices are converted back to real world 3-D coordinates using the voxel dictionary used during the tokenization process. Mean Absolute Error (MAE) metrics were used to measure the accuracy of the proposed system against the ground truth, with the proposed mmPose-NLP offering <3 cm localization errors in the depth, horizontal and vertical axes. The effect of the number of input frames vs performance/accuracy was also studied for N = {1,2,..,10}. A comprehensive methodology, results, discussions and limitations are presented in this paper. All the source codes and results are made available on GitHub for furthering research and development in this critical yet emerging domain of skeletal key-point estimation using mmWave radars.

📄 PDF Abstract BibTeX arXiv:2107.10327

Code (0)

등록된 구현이 없습니다.

Tasks

Autonomous VehiclesDecision MakingKeyword ExtractionPose Estimation

Similar Papers 제목 키워드 기반

Skeleton Detection Using Dual Radars with Integration of Dual-View CNN Models and mmPose

2024-11-28 · Masaharu Kodama, Runhe Huang

Skeleton detection is a technique that can beapplied to a variety of situations. It is especially critical identifying and tracking the movements of the elderly, especially in real-time fall detection. While conventional…

Data Integration

Towards Compact Sign Language Translation: Frame Rate and Model Size Trade-offs

2026-05-10 · Kuanwei Chen, Mengfeng Tsai arxiv

Sign Language Translation (SLT) converts sign language videos into spoken-language text, bridging communication between Deaf and hearing communities. Current gloss-free approaches rely on large encoder-decoder models, li…

Sign Language Translation

POS-tagging to highlight the skeletal structure of sentences

2024-11-21 · Grigorii Churakov

This study presents the development of a part-of-speech (POS) tagging model to extract the skeletal structure of sentences using transfer learning with the BERT architecture for token classification. The model, fine-tune…

Machine TranslationMorphological AnalysisPart-Of-Speech TaggingPOS+5

RTMPose: Real-Time Multi-Person Pose Estimation based on MMPose

2023-03-13 · Tao Jiang, Peng Lu, Li Zhang, Ningsheng Ma 외

Recent studies on 2D pose estimation have achieved excellent performance on public benchmarks, yet its application in the industrial community still suffers from heavy model parameters and high latency. In order to bridg…

2D Human Pose Estimation2D Pose EstimationCPUGPU+2

Contact-Rich and Deformable Foot Modeling for Locomotion Control of the Human Musculoskeletal System

2025-08-16 · Haixin Gong, Chen Zhang, Yanan Sui arxiv

The human foot serves as the critical interface between the body and environment during locomotion. Existing musculoskeletal models typically oversimplify foot-ground contact mechanics, limiting their ability to accurate…