<title>Automatic acquisition of motion trajectories: tracking hockey players</title>
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Computer systems that have the capability of analyzing complex and dynamic scenes play an essential role in video annotation. Scenes can be complex in such a way that there are many cluttered objects with different colors, shapes and sizes, and can be dynamic with multiple interacting moving objects and a constantly changing background. In reality, there are many scenes that are complex, dynamic, and challenging enough for computers to describe. These scenes include games of sports, air traffic, car traffic, street intersections, and cloud transformations. Our research is about the challenge of inventing a descriptive computer system that analyzes scenes of hockey games where multiple moving players interact with each other on a constantly moving background due to camera motions. Ultimately, such a computer system should be able to acquire reliable data by extracting the players’ motion as their trajectories, querying them by analyzing the descriptive information of data, and predict the motions of some hockey players based on the result of the query. Among these three major aspects of the system, we primarily focus on visual information of the scenes, that is, how to automatically acquire motion trajectories of hockey players from video. More accurately, we automatically analyze the hockey scenes by estimating parameters (i.e., pan, tilt, and zoom) of the broadcast cameras, tracking hockey players in those scenes, and constructing a visual description of the data by displaying trajectories of those players. Many technical problems in vision such as fast and unpredictable players' motions and rapid camera motions make our challenge worth tackling. To the best of our knowledge, there have not been any automatic video annotation systems for hockey developed in the past. Although there are many obstacles to overcome, our efforts and accomplishments would hopefully establish the infrastructure of the automatic hockey annotation system and become a milestone for research in automatic video annotation in this domain.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it