MapReduce-based techniques for multiple object tracking in video analytics
Bibliographic record
Abstract
Multiple Object Tracking (MOT) has been deployed effectively in various applications including automated video surveillance, self-driving vehicles, robotics, and medical imaging. Despite improvements in the qualitative performance (accuracy) of the existing state-of-the-art MOT methods through complex image analysis and global optimization techniques, a high computational cost is still a performance limitation. This paper focuses on achieving a high computational speed and proposes three parallel MOT techniques based on MapReduce. This paper introduces techniques that provide a parallel solution which effectively handles the challenges of time-dependencies among the various sections of the video file processed during MOT. Through performance analysis of a prototype deployed on the Amazon EC2 cloud, this paper shows that the proposed techniques provide a scalable solution for parallelizing the MOT methods and achieves an efficiency and speedup of up to 77% and 17 respectively, for a large video file, on a 20 node Hadoop cluster.
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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 | 0.001 | 0.001 |
| 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 | 0.001 | 0.001 |
| Open science | 0.001 | 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".