Joint estimation fusion and tracking of objects in a single camera using EM-EKF
Bibliographic record
Abstract
Tracking objects in dynamic scene is an interesting area of research and it has it's applications in many areas like surveillance, missile tracking system,virtual reality and robot vision. Objects in real world exhibit complex interactions with each other. When captured in a video signal, these interactions manifest themselves as in- tertwineing motions , occlusion and pose changes. A video tracking system should track these objects in this complex interactions smoothly . This paper presents a new joint method for tracking moving objects in outdoor and indoor environment. This joint method uses recursive Expectation-Maximization (EM) incorporated with Extended Kalman Filter (EKF) to estimate, fuse and track the object simultaneously, than doing it in two dif- ferent steps. This combined approach provides more realistic solution to the problem. Thereby, outperforming the conventional method of treating it as three di erent problems. We have tested our algorithm with standard dataset and real time video sequences collected from indoor environment. We also nd that the usage of the joint method improves the accuracy and computational cost. This method successfully tracks object with occlusions, di erent orientations and intertwining motion.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| 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".