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Analysis of°ows in crowd videos is a remarkable topic with practical implementations in many di®erent areas. In this paper, we present a wide overview of this topic along with our own approach to this problem. Our approach treats the di±culty of crowd°ow analysis by distinguishing single versus multiple°ows in a scene. Spatiotemporal features of two consecutive frames are extracted by optical°ows to create a three-dimensional tensor, which retains appearance and velocity information. Tensor's upper left minor matrix captures intensity structure. A normalized continuous rank-increase measure for each frame is calculated by a generalized interlacing property of the eigenvalues of these matrices. In essence, measure values put through the knowledge of existing°ows. Yet they do not go into e®ect desirably due to optical°ow estimation error and some other factors. A proper set of the degree of polynomial tting functions decodes their existence. But how can we estimate that set? Its detailed study is performed. Zero°ow, single°ow, multiple°ows, and interesting events are detected as frame basis using thresholds on the polynomial¯tting measure values. Plausible mean outputs of recall rate (88.9%), precision rate (86.7%), area under the receiver operating characteristic curve (98.9%), and accuracy (92.9%) reported from conducted experiments on PETS2009 and UMN benchmark datasets make clear and visible that our method gains high-quality results to detect°o ws and events in crowd videos in terms of both robustness and potency.

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