Crowd Abnormality Detection Using Optical Flow and GLCM-Based Texture Features

Detection of abnormal crowd behavior is one of the important tasks in real-time video surveillance systems for public safety in public places such as subway, shopping malls, sport complexes and various other public gatherings. Due to high density crowded scenes, the detection of crowd behavior becomes a tedious task. Hence, crowd behavior analysis becomes a hot topic of research and requires an approach with higher rate of detection. In this work, the focus is on the crowd management and present an end-to-end model for crowd behavior analysis. A feature extraction-based model using contrast, entropy, homogeneity, and uniformity features to determine the threshold on normal and abnormal activity has been proposed in this paper. The crowd behavior analysis is measured in terms of receiver operating characteristic curve (ROC) & area under curve (AUC) for UMN dataset for the proposed model and compared with other crowd analysis methods in literature to prove its worthiness. YouTube video sequences also used for anomaly detection.

Crowd behavior analysis using MoDTA approach

<span>In order to analyze the behaviors of human, significant extent of work has been carried out in the video surveillance applications. While considering the crowded scenes, the adopted features are crafted manually which have a great side to detect anomaly. It requires prior information and is hard to extract from complex video scenes and also it involves huge computational costs. In this paper, we are proposing multi-observational detection and tracking approach (MoDTA) that is based on observational filter. The MoDTA initially acquires<span> </span>people location in an image, </span>so that is <span>can detect conviction value at pointed locations which generally increases with respect to people density. In the phase of tracking, MoDTA computes the multiple observed weight values and individual features, also advection particle is used at motion model in order to facilitate the dense scenario tracking. Coefficient of correlation is used as template detector and the function of template detector is to estimate the upcoming object. Our proposed MoDTA is compared with other existing detection and tracking methods in order to evaluate the system performance.<span> </span></span>