2015/08/05 by Francesco Solera, Solera, Francesco, Simone Calderara +3 · 1 citation
Computer Science · Engineering · #Anomaly Detection Techniques and Applications #Computer Vision and Pattern Recognition (cs.CV) #Evacuation and Crowd Dynamics #FOS: Computer and information sciences #Video Surveillance and Tracking Methods #cs.CV
paper · pdf · doi:10.48550/arxiv.1508.01158
openalex publication_date 2015/08/05 · arxiv created 2015/08/06 · arxiv updated 2015/08/07 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Modern crowd theories agree that collective behavior is the result of the underlying interactions among small groups of individuals. In this work, we propose a novel algorithm for detecting social groups in crowds by means of a Correlation Clustering procedure on people trajectories. The affinity between crowd members is learned through an online formulation of the Structural SVM framework and a set of specifically designed features characterizing both their physical and social identity, inspired by Proxemic theory, Granger causality, DTW and Heat-maps. To adhere to sociological observations, we introduce a loss function (G-MITRE) able to deal with the complexity of evaluating group detection performances. We show our algorithm achieves state-of-the-art results when relying on both ground truth trajectories and tracklets previously extracted by available detector/tracker systems.