Alam, Md. AshrafulReza, Md. TanzimNafim, Ilham HoqueTonni, Somiya AzadiRuna, Humira Akter2025-01-162025-01-16©20242024-10ID 20101375ID 20101187ID 20301073http://hdl.handle.net/10361/25188Cataloged from PDF version of thesis.Includes bibliographical references (pages 44-46).This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science, 2024.Criminal activity detection from footage, especially in low-light circumstances, offers a considerable problem due to reduced visibility, noise, and detail loss. In this research, we present an approach for detecting criminal actions in low-light videos using deep learning models. The specific difficulty of low-light conditions, which result in limited visibility and noisy data, is handled using modern video pre-processing techniques to improve video quality. Our strategy improves video classification accuracy by using both spatial and temporal information, preserving essential visual signals. We use transfer learning to adapt pre-trained models such as VGG-16, MobileNetV2, NASNetMobile, LSTM and a Conv-LSTM so that they can generalize effectively in low-light settings. The modified UCF Crime dataset simulates low-light situations, and our results illustrate that the suggested technique improves detection accuracy while remaining efficient. This study paves the path for more dependable surveillance systems capable of working in harsh environments. After working through all these models the best performing model we got was the ConvLSTM model where we got an accuracy of 77%.56 pagesenBRAC University theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission.Deep learningCriminal activity detectionCNNMobileNetV2VGG16Human behavior analysisThreat detectionUCF crimeNeural networks (Computer science).Human activity recognition (Computer vision).Crime prevention--Technological innovations.Crime--Prediction of.Crime--Behavioral assessment.Thesis