Exploring the Effectiveness of Appearance Descriptor in DeepSORT
Bibliographic record
Abstract
Tracking-by-detection approaches have demonstrated their strength in addressing Multiple Object Tracking (MOT) problems. DeepSORT, one of the classical tracking-by-detection MOT methods, relies on a deep appearance descriptor to extract global appearance features of identities. Although the appearance descriptor acts as a key component of such tracking-by-detection methods, which is responsible for modeling appearance information, the relationship between it and tracking performance remains unclear, especially whether further improvements to it will be reflected in the tracking performance. To explore that, extensive experiments are conducted on the appearance descriptor by applying various traditional optimization methods. Furthermore, we propose an Evolutionary Neural Architecture Search (ENAS) strategy for the appearance descriptor named Genetic-SORT to assist exploration. The experimental results demonstrate that tracking performance fails to follow the improvements applied on the appearance descriptor and even shows a negative correlation, which is contrary to our intuition.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".