DETECTION OF ANOMALOUS EVENTS BASED ON DEEP LEARNING-BILSTM

Abstract

Video anomaly detection in smart cities is a critical errand in computer vision that plays an imperative role in intelligent surveillance and public security but is challenging due to its differing, complex, and rare event in real-time surveillance situations. Different deep learning models utilize a critical amount of training data without generalization capabilities and with long time complexity. In this work, and to overcome these problems, an algorithm for reducing the size of the extracted features have been suggested, and this was done by combining every 15 video frames to generate the new features vectors which will be fed into our classifier model, the values of new features vectors represent the summation of the values of original features vectors got from Resnet50. Finally, the new feature vectors are fed into our classifier model to detect the abnormality. We conducted comprehensive tests on a variety of anomaly detection benchmark datasets to verify the proposed framework's functionality in complex surveillance scenarios. The Numerical results were carried out on the UCF-Crime dataset, with the proposed approach achieving Area Under Curve (AUC) scores of 93.61% on the database's test set.