Object detection in real-time video surveillance using attention based transformer-YOLOv8 model

Object detection plays a crucial role in various applications, including surveillance, autonomous driving, and industrial automation, where accurate and timely identification of objects is essential. This research proposes a novel framework that combines the YOLOv8 backbone network with an attention mechanism and a Transformer-based detection head, significantly enhancing object detection performance in real-time images and video. The incorporation of attention mechanisms refines feature extraction from complex scenes, enabling the model to focus on relevant regions within images. Using the integration of Transformer architecture, the model leverages long-range dependencies and global context, leading to more…

• PS
  Prince Sultan University