Learning Robust Features for Planar Object Tracking
Bibliographic record
Abstract
Visual tracking of planar objects with multiple degrees of freedom is a core component for numerous vision-based applications. Generally, since direct methods use all raw pixels in the region of interest to estimate the motion model directly, these methods are sensitive to illumination changes, partial occlusion, and motion blur. Recently, the deep convolutional network has demonstrated remarkable ability in visual tracking via learning robust deep features. In this paper, deep features are used for improving the robustness of direct methods. To learn suitable features in an end-to-end fashion, we employ a novel network architecture, the efficient second-order minimization (ESM) layer, which performs the ESM algorithm on deep feature maps. We train and validate the convolutional features on a synthetic dataset generated from the MS-COCO dataset and evaluate the tracking performance on two challenging, real-world datasets. The experimental results show that the proposed approach outperforms most state-of-the-art methods in various tracking challenges.
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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 | 0.001 | 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 | 0.001 | 0.001 |
| Open science | 0.001 | 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 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".