An Empirical Analysis of Visual Features for Multiple Object Tracking in\n Urban Scenes
Bibliographic record
Abstract
This paper addresses the problem of selecting appearance features for\nmultiple object tracking (MOT) in urban scenes. Over the years, a large number\nof features has been used for MOT. However, it is not clear whether some of\nthem are better than others. Commonly used features are color histograms,\nhistograms of oriented gradients, deep features from convolutional neural\nnetworks and re-identification (ReID) features. In this study, we assess how\ngood these features are at discriminating objects enclosed by a bounding box in\nurban scene tracking scenarios. Several affinity measures, namely the\n$\\mathrm{L}_1$, $\\mathrm{L}_2$ and the Bhattacharyya distances, Rank-1 counts\nand the cosine similarity, are also assessed for their impact on the\ndiscriminative power of the features. Results on several datasets show that\nfeatures from ReID networks are the best for discriminating instances from one\nanother regardless of the quality of the detector. If a ReID model is not\navailable, color histograms may be selected if the detector has a good recall\nand there are few occlusions; otherwise, deep features are more robust to\ndetectors with lower recall. The project page is\nhttp://www.mehdimiah.com/visual_features.\n
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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 | 0.005 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| 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 | 0.000 |
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".