Graph Convolution Neural Network-Based Data Association for Online Multi-Object Tracking

In this paper, a graph convolutional network (GCN)-based multi-object tracking (MOT) algorithm, consisting of a module for extracting the initial features and a module for updating the features, that estimates the affinity between nodes is proposed. The feature extraction module utilizes the pose feature of the object such that the tracking is correct even when the object is partially occluded. Unlike previous graph neural network (GNN)-based MOT methods, this study is based on a GCN and includes a new feature update mechanism, which is updated by combining the output of the neural network and the node similarity between the tracker and detection nodes for each layer. The node feature is updated by aggregating the updated edge feature and the connection strength between the tracker and detection. In each GCN layer, the three networks for the node, edge update, and edge classification were designed to minimize the network parameters to enable faster MOT compared to other GCN-based MOTs. The entire GCN network was designed to learn end-to-end through an affinity loss. The experimental results for the MOT16 and 17 challenge datasets show that the proposed method achieves a superior or similar performance in terms of tracking accuracy and speed compared to state-of-the-art methods, including GCN-based MOT.