  Surveillance footage can catch a wide range of realistic anomalies. This
research suggests using a weakly supervised strategy to avoid annotating
anomalous segments in training videos, which is time consuming. In this
approach only video level labels are used to obtain frame level anomaly scores.
Weakly supervised video anomaly detection (WSVAD) suffers from the wrong
identification of abnormal and normal instances during the training process.
Therefore it is important to extract better quality features from the available
videos. WIth this motivation, the present paper uses better quality
transformer-based features named Videoswin Features followed by the attention
layer based on dilated convolution and self attention to capture long and short
range dependencies in temporal domain. This gives us a better understanding of
available videos. The proposed framework is validated on real-world dataset
i.e. ShanghaiTech Campus dataset which results in competitive performance than
current state-of-the-art methods. The model and the code are available at
https://github.com/kapildeshpande/Anomaly-Detection-in-Surveillance-Videos
