A computer vision approach to vehicle detection, classification, and tracking from UAV data for Indian traffic analysis

Abstract

Traffic surveillance is crucial for road safety and efficiency. This study examines object detection techniques tailored for Indian traffic, highlighting the challenges faced. The aim is to track vehicles, pedestrians, and cyclists using drone-captured data in Indian cities. Urban areas in India grapple with traffic problems due to rapid growth and inadequate infrastructure. Effective monitoring can address these. Recent advancements in object detection, a critical computer vision task, show promise for this application. Employing the YOLOv8 model, trained on our drone-collected dataset, which improved detection accuracy for the Indian Traffic Scenario, is shown. This dataset labelling of vehicle bounding boxes underwent preprocessing using Gaussian Filter, resizing, normalization, and augmentation. Testing showcased the model's real-world applicability in areas like traffic management and autonomous driving. The proposed model thus enhances vehicle detection systems, fostering better safety and decision-making, yielding promising results, as evidenced by 0.86 mAP50 after training and testing the model under own UAV Dataset.