Automatic initialization for broadcast sports videos rectification
Bibliographic record
Abstract
Broadcast sport videos can be captured by a static or a moving camera. Unfortunately, the problem with a moving camera is that planar projective transformations (i.e., the homographies) have to be computed for each image frame in a video sequence in order to compensate for camera motions and viewpoint changes. Recently, a variety of methods have been proposed to estimate the homography between two images based on various correspondences (e.g., points, lines, ellipses matchings, and their combinations). Since the frame to frame homography estimation is an iterative process, it needs an initial estimate. Moreover, the initial estimate has to be accurate enough to guarantee that the method is going to converge to an optimal estimate. Although the initialization can be done manually for a couple of frames, manual initialization is not feasible where we are dealing with thousands of images within an entire sports game. Thus, automatic initialization is an important part of the automatic homography estimation process. In this dissertation we aim to address the problem of automatic initialization for homography estimation. More precisely, this thesis comprises four key modules, namely preprocessing, keyframe selection, keyframe matching, and frame-to-frame homography estimation, that work together in order to automatically initialize any homography estimation method that can be used for broadcast sports videos. The first part removes blurry images and roughly estimates the game-field area within remaining salient images and represents them as a set of binary masks. Then, those resulting binary masks are fed into the keyframe selection module in order to select a set of representative frames by using a robust dimensionality reduction method together with a clustering algorithm. The third module finds the closest keyframe to each input frame by taking advantage of three classifiers together with an artificial neural network to combine their results and improve the overall accuracy of the matching process. The last module takes the input frames, their corresponding closest keyframes, and computes the model-to-frame homography for all input frames. Finally, we evaluate the accuracy and robustness of our proposed method on one hockey and two basketball datasets.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.004 |
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".