Crowd Anomaly Detection Using Machine Learning

Surveillance videos have become crucial objects for ensuring the safety and security of crowded places such as concerts, subways, airports, and many others. In recent times, the installation of security cameras has rapidly increased in both public and private places. The complexity of finding the anomaly will increase drastically as the amount of footage increases and there may be occlusions for the analysis of the video, which consumes both time and may result in false detections. To overcome these drawbacks, we have produced an approach using machine learning algorithms. In this approach, we use the background subtractor for the elimination of the static things in the video frames and concentrate on the dynamic things. Secondly, we extract the spatio-temporal features from the individual frame to increase the efficiency of the detection. This approach is evaluated using the UCSD Dataset, which comprises 50 training and 48 testing samples. The effectiveness of this approach has been testified against metrics such as F1 score, Recall, Precision, and Accuracy