Reliable, Robust, Accurate and Real-Time 2D LiDAR Human Tracking in Cluttered Environment: A Social Dynamic Filtering Approach
Bibliographic record
Abstract
Reliable, robust, accurate, and real-time human tracking is essential for mobile robots and intelligent vehicles in real-life applications. 2D LiDAR is considered as the standard sensor for mobile robot navigation as well as human detection and tracking due to its low-cost and usability. However, 2D range limitation and occlusion caused by obstacles, especially dynamic human environments, make it less reliable, robust and accurate for human tracking. This letter introduces a new method for increasing the quality of 2D LiDAR human tracking in cluttered and crowded environments. We combined human content presented by Hall's Proxemics model with the global nearest neighbor to improve accuracy of scan-to-track data association of leg detection. Social dynamic confidence (SDC) factor is generated based on features of human social norms, dynamic metrics and consistency developed in the detection stage. As a result, our proposed method improved multi-object tracking accuracy and runtime 24% and 45%, respectively, against the state-of-the-artjoint-leg-trackertechnique in crowded and cluttered environments.
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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".