Kainat Aslam2025-12-202025-12-202025https://escholar.umt.edu.pk/handle/123456789/17354Human behavior can be classified into two categories: criminal and non-criminal. Surveillance cameras record activities in their environment, but doing so by themselves becomes challenging and exausting. By working through these problems, we attempt to create an intelligent surveillance system that can distinguish and identify illegal behaviors and gestures without requiring constant human observation. Over the years, deep learning models have transformed image analysis by utilizing multi-layer neural networks. Depending on their architecture, all of these models facilitate considerable improvements in model performance and accuracy. We put forward a method that integrates the LSTM model with the layers that allowed for the identification of temporal patterns within the frames and captures high-level characteristics for important identification of anomalies on the DenseNet121 model. Using the UCF Crime data set, experiments demonstrated that our method outperformed current state-of-the-art methods (75% AUC) and accuracy of 73%. In order to implement complete testing and thorough model training across various anomalous occurrences, we enhanced the data.enThesis