  In crowded scenes, detection and localization of abnormal behaviors is
challenging in that high-density people make object segmentation and tracking
extremely difficult. We associate the optical flows of multiple frames to
capture short-term trajectories and introduce the histogram-based shape
descriptor referred to as shape contexts to describe such short-term
trajectories. Furthermore, we propose a K-NN similarity-based statistical model
to detect anomalies over time and space, which is an unsupervised one-class
learning algorithm requiring no clustering nor any prior assumption. Firstly,
we retrieve the K-NN samples from the training set in regard to the testing
sample, and then use the similarities between every pair of the K-NN samples to
construct a Gaussian model. Finally, the probabilities of the similarities from
the testing sample to the K-NN samples under the Gaussian model are calculated
in the form of a joint probability. Abnormal events can be detected by judging
whether the joint probability is below predefined thresholds in terms of time
and space, separately. Such a scheme can adapt to the whole scene, since the
probability computed as such is not affected by motion distortions arising from
perspective distortion. We conduct experiments on real-world surveillance
videos, and the results demonstrate that the proposed method can reliably
detect and locate the abnormal events in the video sequences, outperforming the
state-of-the-art approaches.
