Leveraging Transfer Learning and YOLO for Scalable Anomaly Detection in Surveillance Systems

This project investigates the use of advanced deep learning methods, particularly Transfer Learning and the YOLO (You Only Look Once) algorithm, to distinguish between normal and abnormal behaviors in complex, real-world settings. Detecting anomalous behavior demands a robust approach, especially when limited labeled data is available for training. Traditional anomaly detection systems often face challenges in such environments, requiring extensive computational resources and suffering from decreased accuracy when faced with complex or unpredictable inputs. Transfer Learning addresses these limitations by enabling models to leverage knowledge gained from related tasks, allowing for faster adaptation and reduced training requirements while improving detection precision. In this work, Transfer Learning equips the model with foundational understanding, enabling swift responses to new, varied inputs. When combined with the YOLO algorithm—known for its real-time object detection capabilities—the system can process live video feeds, identifying unusual behaviors with remarkable efficiency and accuracy. YOLO's architecture processes entire images in a single pass, making it a prime candidate for real-time anomaly detection. This hybrid approach of Transfer Learning and YOLO optimizes both sensitivity and speed, allowing the model to adapt across diverse environments while achieving superior detection performance. Ultimately, this project aims to advance public safety by enhancing systems capable of identifying potentially harmful behaviors swiftly and accurately. By innovatively integrating these deep learning techniques, we contribute to ongoing efforts in public surveillance and safety, where timely identification of abnormal events is crucial. This work underscores the potential of combining deep learning methodologies with real-time analysis tools to address contemporary social security challenges, paving the way for safer, more responsive public spaces. The integration of Transfer Learning and YOLO in this project not only highlights the potential for enhancing machine learning models but also addresses critical gaps in current anomaly detection frameworks. While traditional methods may perform adequately in controlled environments, they often struggle to generalize to real-world scenarios where unpredictable events and limited data availability are common. By leveraging Transfer Learning, the model can rapidly adapt to new contexts without the need for exhaustive retraining, making it particularly suitable for dynamic and resource-limited applications. Additionally, the YOLO algorithm’s capability for real-time processing ensures that the system can respond to anomalous behaviors as they occur, allowing for timely intervention and mitigating potential risks. This research has broader implications for various industries, including public security, healthcare, and infrastructure monitoring, where early detection of abnormal patterns is essential. Ultimately, this project contributes to the growing field of AI-driven anomaly detection, paving the way for more responsive and adaptable systems capable of promoting social well-being and safety.