Quantifying Path Smoothness in Video Object Tracking by Detection
Bibliographic record
Abstract
Object detection and tracking are important areas of research in computer vision. Computer vision solutions to object detection are typically single-frame solutions. To perform tracking by detection, these solutions typically do object detection on a perframe basis, thus losing any temporal information from previous frames. Many multiobject tracking solutions report the average precision performance on video datasets, but they do not evaluate the temporal qualities of these solutions. In video, not only the detection of objects is important but the temporal motion attributes of an object’s path, such as its velocity, acceleration, and jerk, are important as well. Many implementations of Object Tracking by Detection systems have run into the problem of motion smoothing for bounding box paths. This paper focuses on quantifying the smoothness of detected object paths within some temporal window. We propose using two smoothness metrics from the field of biokinematics and adapt them for use with detections. Finally, using these metrics, we evaluate the ground truth and two popular object detectors, at the time of experimentation (YOLOv3 and Retinanet), on the entire MOT17 dataset. The results show that the metrics are useful in determining object smoothness, and provide us with an additional approach to evaluate an algorithm’s performance in object tracking. The experiments also demonstrate that YOLOv3 produces smoother bounding boxes than Retinanet. All supplemental graphs and data are shown in our appendix
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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 | 0.003 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 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".