Abstract
Collective behaviors of coherent groups convey semantic relations among individuals in a crowd scene. Detecting coherent groups is primitive for crowd behavior analysis and practically useful in crowd surveillance. However, classically, crowd analysis in still image is focused on crowd counting estimation or crowd segmentation only. In this paper, we present a novel framework that merges these two classical approaches to achieve a higher level of crowd understanding, i.e., detecting coherent groups within a crowd in a still image. Essentially, in addition to crowd counting estimation, our work can infer crowd segments at both image-level and coherent group level. Extensive experiments and analysis of crowd scene images with a variety of crowd densities demonstrate the efficacy of the proposed framework. The proposed framework also shows its potential application, especially in crowd understanding.
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