MODFAT: Moving Object Detection by Removing Shadow Based on Fuzzy Technique with an Adaptive Thresholding Method
Bibliographic record
Abstract
Nowadays, in this technology centric world, gadgets have become handy due to miniaturization. Especially cameras are widely used device for many aspects, one of the common applications is human behavior identification and intelligent video surveillance. In a such application moving object detection in complex dynamic scene is a tedious task due to various challenges such as occlusion, background illumination variation and shadow. Shadows are created in light occlusion in the object it has major impact in accurate object detection. In this paper, object detection with elimination of shadow is addressed. Many existing methods have failed in discriminating the actual moving object from shadow object very accurately. In order to overcome the limitations of existing methods, an improved fuzzy technique rule is used for shadow removal and an adaptive fuzzy thresholding is used for segmenting a foreground object in background. The proposed techniques are experimented with standard and our own datasets and also, it is compared with other existing approaches. Results of proposed method shows improved reliability.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".