Vehicle detection using TD2DHOG features
Bibliographic record
Abstract
Histogram of oriented gradients (HOG) is often used for object detection in images. These HOG features of images can be referred to as 2DHOG when represented in a 2D matrix format instead of a 1D vector. In this paper, we propose a new vehicle detection algorithm by using 2DHOG in the discrete cosine transform (DCT) domain. The proposed technique consists of extracting 2DHOG from the input image and applying on it 2DDCT. This is followed by a low pass filtering in order to obtain novel features called as transform-domain 2DHOG (TD2DHOG). TD2DHOG is used with a classifier pyramid in order to reduce the multi-scale scanning cost. Experimental results show that the proposed algorithm when applied on two public vehicle detection datasets reduces the storage requirement of the classifier pyramid, while providing about the same performance as that provided by the state-of-the-art techniques.
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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 | 0.000 | 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 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".