Online Multiple-Pedestrian Tracking With Detection-Pair-Based Graph Convolutional Networks
Bibliographic record
Abstract
The typical Internet of Things application, unattended driving systems, will need the ability to recognize relevant traffic participants and detect dangerous situations ahead of time. An important component of these systems is one that is able to distinguish pedestrians and track their motion to make intelligent driving decisions. This article develops a high-accuracy multiple pedestrian tracking algorithm which is vital for intelligent transportation. Here, we use the off-the-shelf detectors and explore the benefits of modeling pedestrian interactions, such as the interaction of two pedestrians simultaneously matched to two pedestrians in another frame, for robust detection association. Explicitly studying interactions is nontrivial. Previous works often manually selected interacting detections (or “tracklets”) to simplify the association process. In this article, we propose a novel association method based on deep graph convolutional affinity networks (DGCANs) and extend detection-level interactions to the association-level, which treats a potential association of a detection pair as a node in the graph, and explicitly modeling the interactions among potential associations. Specifically, with the novel node, two corresponding edges are readily designed to model the compatible and colliding interactions between related associations. Our proposed method, by redefining nodes and edges, enables us to blend sufficient interaction cues from appearance and motion and learns a robust affinity measure in an end-to-end fashion. Using the Hungarian algorithm as an online tracker, our method archives state-of-the-art performance on benchmark data sets 2-D MOT15, MOT16, and MOT17.
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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 | 0.000 |
| 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".