Cascaded particle filter for real-time tracking using RGB-D sensor
Bibliographic record
Abstract
This paper presents a real-time human body tracking system based on cascaded particle filter using Microsoft Kinect. Our tracking is performed in two layers: RGB image-based 2D region body tracking and 1D depth image body extremities tracking. These two layers are combined to represent the basis for full 3D human body tracking by obtaining a probable human body bounding box in 2D while 3D position is obtained by incorporating available depth information. As the first layer, we utilized motion history for tracking a walking person. One particle filter is used to track the 3D bounding box of the person in synchronization with RGB and depth video stream. For this layer, we utilized the expected motion constraints for enhancing the distribution of particle in importance sampling stage. For the second layer within the bounding box, we use depth information to track the extremities. State of the points of extremities is defined and another particle filter is implemented which utilizes a measure using the notion of spin image. The proposed framework can be used to evaluate and compare estimates at any given instances. For example, the initial estimates associated with the tracking of the whole body can be used as a coarse measure for the tracking of the local extremities in case of self-occlusions.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".