  Accounting for the increased concern for public safety, automatic abnormal
event detection and recognition in a surveillance scene is crucial. It is a
current open study subject because of its intricacy and utility. The
identification of aberrant events automatically, it's a difficult undertaking
because everyone's idea of abnormality is different. A typical occurrence in
one circumstance could be seen as aberrant in another. Automatic anomaly
identification becomes particularly challenging in the surveillance footage
with a large crowd due to congestion and high occlusion. With the use of
machine learning techniques, this thesis study aims to offer the solution for
this use case so that human resources won't be required to keep an eye out for
any unusual activity in the surveillance system records. We have developed a
novel generative adversarial network (GAN) based anomaly detection model. This
model is trained such that it learns together about constructing a high
dimensional picture space and determining the latent space from the video's
context. The generator uses a residual Autoencoder architecture made up of a
multi-stage channel attention-based decoder and a two-stream, deep
convolutional encoder that can realise both spatial and temporal data. We have
also offered a technique for refining the GAN model that reduces training time
while also generalising the model by utilising transfer learning between
datasets. Using a variety of assessment measures, we compare our model to the
current state-of-the-art techniques on four benchmark datasets. The empirical
findings indicate that, in comparison to existing techniques, our network
performs favourably on all datasets.
