Real-Time Object Identification Using Coco Dataset

Abstract

Real-time object identification is a crucial aspect of computer vision with applications in robotics, autonomous vehicles, and intelligent systems. The MS COCO (Common Objects in Context) dataset provides a rich resource for training and evaluating object detection models. This paper explores the utilization of the COCO dataset for real-time object identification. We discuss the strengths of COCO for this purpose, including its diverse image collection, comprehensive object annotations, and established benchmarks for performance evaluation. We then examine various approaches to real-time object identification using the COCO dataset. This may involve exploring pre-trained models like YOLO or SSD or customizing architectures for specific real-time constraints. The paper will analyze the trade-offs between accuracy and processing speed when deploying these models for real-time applications. Finally, we address the challenges associated with real-time object identification, such as object occlusion, background clutter, and variations in lighting conditions. We leverage the COCO dataset to present advancements and potential solutions for robust real-time object identification in diverse scenarios.