Measuring Crowd Collectiveness by Macroscopic and Microscopic Motion Consistencies

As a scene-independent descriptor of crowd motions, crowd collectiveness quantifies the degree of constituent individuals moving as a union in a crowd scene. An effective measurement on crowd collectiveness is of great importance for applications in surveillance of public safety, human dynamics, and other areas. To this end, we propose a novel framework to measure crowd collectiveness by combining macroscopic and microscopic motion consistencies and define quantitatively the global and local consistency of crowd motions. The defined global consistency represents the likelihood of pairwise individuals belonging to the same collective group, whereas the local consistency reflects the degree of conformity in a local region. Based on the proposed collectiveness measure, a new algorithm, named group mining, is proposed to detect collective groups from a crowd. We validate the effectiveness of the proposed method on several synthetic particle systems and a real-world crowd database with human-labeled collectiveness. Experimental results show that, compared with the previous approaches, our collectiveness measure is more consistent with human perception, and the collective groups detected by our group mining algorithm are more accurate and robust.