Through-Wall Object Recognition and Pose Estimation
Bibliographic record
Abstract
Through-Wall Object Recognition and Pose Estimation Ruoyu Wang, Siyuan Xiang, Chen Feng, Pu Wang, Semiha Ergan and Yi Fang Pages 1176-1183 (2019 Proceedings of the 36th ISARC, Banff, Canada, ISBN 978-952-69524-0-6, ISSN 2413-5844) Abstract: Robots need to perceive beyond lines of sight, e.g., to avoid cutting water pipes or electric wires when drilling holes on a wall. Recent off-the-shelf radio frequency (RF) imaging sensors ease the process of 3D sensing inside or through walls. Yet unlike optical images, RF images are difficult to understand by a human. Meanwhile, in practice, RF components are often subject to hardware imperfections, resulting in distorted RF images, whose quality could be far from the claimed specifications. Thus, we introduce several challenging geometric and semantic perception tasks on such signals, including object and material recognition, fine-grained property classification and pose estimation. Since detailed forward modeling of such sensors is sometimes difficult, due to hidden or inaccessible system parameters, onboard processing procedures and limited access to raw RF waveform, we tackled the above tasks by supervised machine learning. We collected a large dataset of RF images of utility objects through a mock wall as the input of our algorithm, and the corresponding optical images were taken from the other side of the wall simultaneously as the ground truth. We designed three learning algorithms based on nearest neighbors or neural networks, and report their performances on the dataset. Our experiments showed reasonable results for semantic perception tasks yet unsatisfactory results for geometric ones, calling for more efforts in this research direction. Keywords: Through-Wall Imaging; ObjectRecognition; Pose Estimation; Deep Learning DOI: https://doi.org/10.22260/ISARC2019/0157 Download fulltext Download BibTex Download Endnote (RIS) TeX Import to Mendeley
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.007 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".