Pedestrian Crosswalk Safety at Intersections using YOLOv8 Detection

Pedestrian safety is a topic that has been tackled by many researchers in the past using machine learning and artificial intelligence approaches. One of the main reasons for this is the development of self-driving vehicles, which would require systems to detect pedestrians to ensure their safety. Many approaches utilize onboard camera systems which work great for most situations but there are certain shortcomings to this approach. One case is with blind right turns where the onboard cameras cannot see the entire crosswalk due to obstruction of a parked vehicle or building close to the corner. An alternative approach is to use cameras that already exist at intersections to create a warning system that detects pedestrians at each crosswalk. There are many methods of implementation, such as integration with self-driving vehicles using a vehicle-to-infrastructure (V2I) approach or using a caution light to warn drivers in advance. This paper will discuss approaches and considerations for using object detection for intersection safety using cameras at intersections. Results using different pre-trained YOLOv8 models provided by Ultralytics for three different intersection datasets from Urban Tracker: Multiple Object Tracking in Urban Mixed Traffic show that the YOLOv8 pre-trained models perform extremely well for this task.