DATA LABELLING
Project Description: Traffic Scene Object Detection Data Labeling 1. Project Overview and Scope This project involved large-scale data labeling for a computer vision system designed to detect and classify road users in urban traffic environments. The primary goal was to create high-quality annotated datasets to train and evaluate an AI-based object detection model for intelligent transportation systems, autonomous driving support, and traffic monitoring applications. The scope of the project covered: Urban road traffic imagery captured from fixed surveillance cameras. Annotation of multiple object categories including vehicles, motorcycles, bicycles, pedestrians, and three-wheeled transport. Bounding box annotation for real-time object detection model training. Ensuring accurate spatial localization and class categorization. Supporting model development for congestion monitoring, safety analytics, and traffic flow optimization. The project focused on scenes with high object den