A novel approach for finding the movement of an object in video sequences by an artificial neural network for 2.5D object modeling
Bibliographic record
Abstract
In this paper we propose a system that identifies and tracks the movement of an object appearing fully or partially hidden by occlusion in a video sequence for the ultimate purpose of modeling the moving object in 2.5D space using the SfM (structure from motion) concepts. This paper presents a novel algorithm to detect moving objects in video sequences by first performing image segmentation on the frame sequences based on the criteria of motion, and then applying a motion vector estimation algorithm to find geometrically identical points in two consecutive video frames. An ANN (artificial neural network) based model was adopted to segment the moving object(s) out of the stationary background. The next step involves applying motion vector search on the motion segmented images to obtain a correspondence between a pixel of the object in the reference frame and a pixel in the subsequent frame such that the pixels corresponds to the same part and geometrical location of the object. Results from various video sequences of motion based segmentation using ANN and the subsequent motion vector estimation have been presented in this paper. Eventually, a wire-frame diagram is constructed to represent a moving object in 2D.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".