Learning Directed Intention-driven Activities using Co-Clustering

We present a novel approach for discovering directed intention-driven pedestrian activities across large urban areas. The proposed approach is based on a mutual information co-clustering technique that simultaneously clusters trajectory start locations in the scene which have similar distributions across stop locations and vice-versa. The clustering assignments are obtained by minimizing the loss of mutual information between a trajectory start-stop association matrix and a compressed co-clustered matrix, after which the scene activities are inferred from the compressed matrix. We demonstrate our approach using a dataset of long duration trajectories from multiple PTZ cameras covering a large area and show improved results over two other popular trajectory clustering and entry-exit learning approaches.