Surveillance systems often face the challenge of managing extensive amounts of footage, much of which is irrelevant, leading to inefficient storage and difficulty in event retrieval. This thesis addresses this issue by proposing an optimized video recording solution that focuses on activity detection. The proposed approach utilizes a hybrid method combining motion detection via frame subtraction and object detection using You Look Only Once model . This strategy aims to record only scenes with human activity, thereby reducing unnecessary footage and optimizing storage usage. The developed model demonstrates superior performance, achieving precision metrics of 0.855 for car detection and 0.884 for person detection, highlighting its effectiveness in enhancing the efficiency of surveillance systems. However, some limitations remain, such as false positives and false negatives in bad weather conditions like powerful winds.