Deep Multiple Instance Learning for Human Trajectory Identification

Zipei Fan, Quanjun Chen, Renhe Jiang, Ryosuke Shibasaki, Xuan Song, Kota Tsubouchi

Abstract: Extracting identifiable information from human trajectories is a fundamental task in many location-based services (LBS). However, various mobility patterns underlain in human trajectories are difficult to model by existing models. Moreover, we could hardly define a clear user set for user identification because the set of users are dynamic and changing everyday. Bearing these in mind, we apply a deep multiple instance learning method to handle the multimodal mobility patterns in a weak-supervised learning way, and address the dynamic user set problems via a pairwise loss with negative sampling. We utilize a multi-head attention mechanism to automatically extract multiple aspects and match the corresponding information between query trajectories and historical trajectories. Our method shows a good identification accuracy on three human GPS trajectory data sets comparing with baseline methods.