An Automated Monitoring System for Abnormal Event Detection

Public events are source of entertainment for every people. Number of such events have been increased with increase in population. Security is the main concern of people especially in such events. Multiple surveillance systems have been utilized to keep track of security concerns and keep an eye on activities of crowd. These systems help to track people and identify suspicious activities or unexpected events occurs. In this paper, a novel method has been proposed to detect abnormal activity occurred at any in-door/out-door environment. Initially, Gaussian filter has been used as preprocessing to detect the foreground objects and background removal. Then fuzzy c-mean has been opted to verify human silhouettes and shadow removal has been performed. After that novel method for region shrinking has been implemented to isolate occluded humans and feature descriptor comprised of velocity and wavelet analysis has been extracted for each silhouettes. Feature has been optimized using Gray Wolf optimizer and abnormal event classification is performed using the XG-Boost classifier. Performance is evaluated using UMN dataset and UBI-Fight dataset, each having different a nature of anomaly. The mean accuracy for the UMN and UBI-Fight datasets is 90.14%, and 81.3% respectively. These results are more accurate as compared to other existing methods.