Video Scene Segmentation Using Tensor-Train Faster-RCNN for Multimedia IoT Systems
Bibliographic record
Abstract
Video surveillance techniques like scene segmentation are playing an increasingly important role in multimedia Internet-of-Things (IoT) systems. However, existing deep learning-based methods face challenges in both accuracy and memory when deployed on edge computing devices with limited computing resources. To address these challenges, a tensor-train video scene segmentation scheme that compares the local background information in regional scene boundary boxes in adjacent frames is proposed. Compared to the existing methods, the proposed scheme can achieve competitive performance in both segmentation accuracy and parameter compression rate. In detail, first, an improved faster region convolutional neural network (faster-RCNN) model is proposed to recognize and generate a large number of region boxes with foreground and background to achieve boundary boxes. Then, the foreground boxes with sparse objects are removed and the rest are considered as optional background boxes used to measure the similarity between two adjacent frames. Second, to accelerate the training efficiency and reduce memory size, a general and efficient training way using tensor-train decomposition to factor the input-to-hidden weight matrix is proposed. Finally, experiments are conducted to evaluate the performance of the proposed scheme in terms of accuracy and model compression. Our results demonstrate that the proposed model can improve the training efficiency and save the memory space for the deep computation model with good accuracy. This work opens the potential for the use of artificial intelligence methods in edge computing devices for multimedia IoT systems.
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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 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".