Advanced AI Techniques for Autonomous Moving Object Detection and Tracking

In the domain of autonomous vehicle systems, the accurate detection and tracking of moving objects, particularly vehicles, are of paramount importance for ensuring safe and efficient operations. Conventional methods based solely on image information face substantial challenges when confronted with complex and dynamic environments. To address these challenges, this research utilizes deep learning (DL) techniques for vehicle detection and tracking. The study draws upon data from the UA-DETRAC dataset, a valuable resource providing real-world scenarios for experimentation. For vehicle detection, DL models, including Faster Region Convolutional Neural Network (Faster R-CNN), You Only Look Once (YOLO), and Single Shot MultiBox Detector (SSD), are employed. These models are assessed using essential metrics like mAP (mean Average Precision), IoU (Intersection over Union), Precision, and Recall. When it comes to object tracking, the study delves into the capabilities of the SORT and DeepSORT algorithms, which represent advanced tracking solutions. Evaluation metrics like MOTA (Multiple Object Tracking Accuracy), IDF1 (Identification F1 score), MT (Mostly Tracked), and ML (Mostly Lost), offer a comprehensive view of tracking performance. The outcomes of this research indicate that YOLO stands out as the top performer in vehicle detection, while DeepSORT excels in the critical task of vehicle tracking. This study’s findings contribute significantly to the advancement of AI techniques, enhancing the capabilities of autonomous systems for object detection and tracking in complex and dynamic real-world environments.