In a realm where security and surveillance hold paramount importance, pioneering work strives to redefine the landscape of surveillance technology. The aim of this research is to address the challenges faced by traditional surveillance systems, including inaccurate human detection, manual data logging errors, and time-consuming retrieval processes for evidentiary footage. This method focused on human-centric solutions, prioritizing precise human detection, automated data logging, and swift snapshot capture. Through rigorous testing and implementation, this work achieved notable results in reducing errors and delays, thereby enhancing security responsiveness effectively. In conclusion, this approach offers a promising solution to the challenges in surveillance technology, paving the way for more efficient and reliable security systems’ dataset, this creates flexibility for supporting other languages.