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Online Multiple-Pedestrian Tracking With Detection-Pair-Based Graph Convolutional Networks
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AI
DOI：10.1109/JIOT.2022.3195359.png)
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摘要
En 中文
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.
Keyword:
Image edge detection
Internet of Things
Tracking
Pipelines
Convolution
Trajectory
Task analysis
Graph convolutional network (GCN)
intelligent transportation
multiple pedestrian tracking
online tracking
期刊
IF:
8.9
论文数:
1.4W
被引数:
7.8W
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