T-DANTE: Detecting Group Behaviour in Spatio-Temporal Trajectories Using Context Information

The present study addresses the group detection problem using spatio-temporal data. This study relies on modeling contextual information embedded in the trajectories of surrounding agents as well as temporal dynamics in the trajectories of the agent of interest to determine if two agents belong to the same group. Specifically, our proposed method, called T-DANTE, builds upon the Deep Affinity Network (DANTE) [16] for Clustering Conversational Interactants using spatio-temporal data and extends it by incorporating Recurrent Neural Networks (RNN) (i.e., Long Short-term Memory (LSTM) and Gated Recurrent Unit (GRU)) to capture the temporal dynamics inherent in the trajectories of agents. Our ablation study demonstrates that including context information, combined with temporal dynamics, yields promising results for the group detection task across five real-world pedestrian and five simulation datasets using two common evaluation metrics, namely Group Correctness and Group Mitre metrics. Moreover, in the comparative study, the proposed method outperformed three state-of-the-art baselines in terms of the group correctness metric by at least 17.97% for pedestrian datasets. Although some baselines perform better in simulation datasets, the difference is not statistically significant.