Abandoned Object Detection and Classification Using Deep Embedded Vision

One indispensable element within security systems deployed at public venues such as airports, bus stops, train stations, and marketplaces is video surveillance. The evolution of more robust and efficient automated technological solutions for video surveillance is imperative. In light of the escalating global threat of terrorist attacks in recent years, any unattended object in public areas is treated as potentially suspicious. Ensuring the protection of individuals in these public spaces necessitates the implementation of safety measures. The intricacies of surveillance recordings introduce challenges when it comes to identifying abandoned or removed objects, owing to factors like occlusion, abrupt lighting changes, and other variables. This paper proposes a novel two-stage method for identifying and locating stationary objects in public settings. The first stage uses a sequential model to capture temporal features and detect potential abandoned objects within the monitored area. When the sequential model detects such an object, it triggers a subsequent phase. The second stage uses the YOLOv8l model to precisely locate the detected objects. YOLOv8l is renowned for its ability to accurately pinpoint object locations within the surveillance scene. The proposed method achieves remarkable accuracy rates of 99.20% and 99.70% on combined PETS 2006 and ABODA datasets, respectively, effectively localizing the target object. This achievement not only underscores the model's precision in accurately pinpointing the object's position within the given context but also establishes its superiority over other existing models. By integrating these two stages, our method provides an effective solution for enhancing the detection of abandoned objects in public spaces, contributing to improved security and safety measures.