Analyzing and Predicting Crowd Behavior using Machine Learning
Keywords:
Communities, Crowd, Behaviours, Emotions, Low-Level, Labels, MotionAbstract
Training a model of crowd behaviour using data taken from video sequences is essential in crowd behaviour comprehension. Most of the existing approaches rely only on low-level visual characteristics since crowd datasets do not include any ground-truth other than the crowd behaviour labels. But the conceptual gap between basic motion/appearance characteristics and the abstract idea of crowd behaviour is enormous. We provide an attribute-based approach to address this issue in this study. We believe our work is the first to demonstrate that crowd emotions may be used as features for understanding crowd behaviour, even if comparable approaches have been utilised for object and action detection in recent times. To reflect the movement of the crowd, the primary goal is to train a classifier set that is based on emotions. To that end, we amass a large collection of video clips and annotate them with "crowd behaviours" and "crowd emotions" tags. We provide the outcomes of the suggested approach on our dataset, which show that crowd emotions allow for the development of richer descriptions of crowd behaviours. Along with the publication, we want to share the dataset so that communities may utilise it as a standard.
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