MVSparse: Distributed Cooperative Multi-camera Multi-target Tracking on the Edge
Why this work is in the frame
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Bibliographic record
Abstract
Tracking people in multi-camera surveillance systems is challenging due to disparate perspectives, large volumes of data, and high computation demands. This paper presents a distributed cooperative pipeline for pedestrian tracking that exploits the spatial and temporal redundancy within and across the video feeds from multiple synchronized cameras. It consists of three key components: 1) a lightweight policy network trained online in a self-supervised manner on each camera, 2) a sparse backbone processing unit purpose-built for parallel processing of selected regions of all cameras, and 3 an online clustering algorithm for object association. Utilizing online distributed reinforcement learning, the fully end-to-end trainable pipeline can accelerate any tracking-by-detection method by reducing detection costs across multiple perspectives. MVSparse has been evaluated using two multi-camera multi-target pedestrian tracking datasets, WildTrack and MultiviewX. It reduces the amount of processed regions by up to 52% and 39% with only moderate degradation of 1% and 0.1% in tracking accuracy on the two datasets, respectively. On a real-world testbed comprising four NVIDIA Jetson TX2 and a GPU server, MVSparse accelerates the end-to-end process and reduces the communication overheads by 1.88 and $1.60 X$ with only 2.27% and 3.17% degradation in tracking accuracy on the two datasets, respectively
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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 | 0.000 | 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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 it