In this paper we propose a novel approach to detect anomalies in crowded scenes. This is achieved by analyzing the crowd
behavior by extracting the corner features. For each corner feature we collect a set of motion features. The motion features
are used to train an MLP neural network during the training stage, and the behavior of crowd is inferred on the test samples.
Considering the difficulty of tracking individuals in dense crowds due to multiple occlusions and clutter, in this work we
extract corner features and consider them as an approximate representation of the people motion. Corner features are then
advected over a temporal window through optical flow tracking. Corner features well match the motion of individuals and
their consistency, and accuracy is higher both in structured and unstructured crowded scenes compared to other detectors.
In the current work, corner features are exploited to extract motion information, which is used as input prior to train the
neural network. The MLP neural network is subsequently used to highlight the dominant corner features that can reveal
an anomaly in the crowded scenes. The experimental evaluation is conducted on a set of benchmark video sequences
commonly used for crowd motion analysis. In addition, we show that our approach outperforms a state of the art technique
In this paper, we propose a fast and robust framework for anomaly detection in crowed scenes. In our method, anomaly is adaptively modeled as a deviation from the normal behavior of crowd observed in the scene. For this purpose, we extract motion features by repeatedly initializing a grid of particles over a temporal window. These features are exploited in a real-time anomaly detection system. In order to model the ordinary behavior of the people moving in the crowd, we use the Gaussian mixture model (GMM) technique, which is robust enough to capture the scene dynamics. As opposed to explicitly modeling the values of all the pixels as a mixture of Gaussians, we adopted the GMM to learn the behavior of the motion features extracted from the particles. Based on the persistence and the variance of each Gaussian distribution, we determine which Gaussians can be associated to the normal behavior of the crowd. Particles with motion features that do not fit the distributions representing normal behavior are signaled as anomaly, until there is a Gaussian able to include them with sufficient evidence supporting it. Experiments are extensively conducted on publically available benchmark dataset, and also on a challenging dataset of video sequences we captured. The experimental results revealed that the proposed method performs effectively for anomaly detection.