Alam, Md. Golam RabiulMiah, ParvezHaque, Abrar AhbabulImran, Abdullah AlHassan, MD. RadipRahman, Rafiur2023-05-252023-05-2520232023-01ID 19101339ID 19301040ID 19101482ID 19101524ID 19101461http://hdl.handle.net/10361/18331Cataloged from PDF version of thesis.Includes bibliographical references (pages 35-36).This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science, 2023.Surveillance camera systems have been implemented in most parts of the world to combat the rising rate of criminal activities. In the hopes of making public places safer for everyone, computer vision has also aided in making these systems more sophisticated but reliable and efficient. However, we are yet to make them better. While modern systems are able to record incidents, they often do not do so intelligently in order to make it easier for law reinforcements to respond quickly enough to aid victims or stop more crimes from occurring. Hence for our thesis project, we intend to use computer vision on a surveillance system so that it is able to identify crimes such as physical altercation, harassment, hijacking, snatching, etc. In this model, an action recognition system will be used, where we will be using extracted images from video feeds from multiple sources, and all those sources (cameras) will be centrally connected to a server. The server will be connected to databases containing information about violent activities. Based on the feeds, a signal will be sent to the respective system if a particular activity is detected. This system is mainly based on image processing concepts using different neural networks like MobileNet-V2, ResNet50, and LSTM to match live images with the existing trained system. This model will specifically use to detect criminal activities such as punching, kicking, slapping, and weapon violence, and all these pieces of information will be previously stored in the database. We have also implemented Grad-CAM in an effort to apply model explain ability.36 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 learningMobileNet-V2ResNet50Surveillance cameraSecuritySuspicious activityCNNLSTMGrad-CAMMachine learningCognitive learning theoryThesis