Online Illumination Invariant Moving Object Detection by Generative\n Neural Network
Bibliographic record
Abstract
Moving object detection (MOD) is a significant problem in computer vision\nthat has many real world applications. Different categories of methods have\nbeen proposed to solve MOD. One of the challenges is to separate moving objects\nfrom illumination changes and shadows that are present in most real world\nvideos. State-of-the-art methods that can handle illumination changes and\nshadows work in a batch mode; thus, these methods are not suitable for long\nvideo sequences or real-time applications. In this paper, we propose an\nextension of a state-of-the-art batch MOD method (ILISD) to an\nonline/incremental MOD using unsupervised and generative neural networks, which\nuse illumination invariant image representations. For each image in a sequence,\nwe use a low-dimensional representation of a background image by a neural\nnetwork and then based on the illumination invariant representation, decompose\nthe foreground image into: illumination change and moving objects. Optimization\nis performed by stochastic gradient descent in an end-to-end and unsupervised\nfashion. Our algorithm can work in both batch and online modes. In the batch\nmode, like other batch methods, optimizer uses all the images. In online mode,\nimages can be incrementally fed into the optimizer. Based on our experimental\nevaluation on benchmark image sequences, both the online and the batch modes of\nour algorithm achieve state-of-the-art accuracy on most data sets.\n
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".