Object Inter-camera Tracking with Non-overlapping Views: A New Dynamic Approach
Bibliographic record
Abstract
Disjoint inter-camera object tracking is the task of tracking objects across video-surveillance cameras that have non-overlapping views. Unlike the closely related task of single-camera tracking, disjoint inter-camera tracking is difficult due to the gaps in observation as an object moves between camera views. To overcome this problem, appearance profiles of the objects seen in each camera are built and used for matching/tracking across different cameras. This paper proposes a new method that uses multiple features that are dynamically weighed for matching moving objects (people in our case) across cameras. In particular, the Zernike moment shape descriptor has been used together with blob histogram and other features to describe a moving object. Weighting emphasis is given to the better features, based on their stability, reliability and their time in the system (how recent they are). This weighting is used both during appearance aggregation and object comparison. Our experiments with real videos have shown the success of our proposed method even in difficult situations where the cameras used are different in terms of brand, quality and resolution.
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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 | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| 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".