Multi-Object Detection and Tracking Using Machine Learning

In today’s information-based economy, data serves as a replacement for oil. There have been shifts in how quickly and accurately benchmarks are measured as a result of efficient data. Industry buzzwords Computer Vision (CV) and Artificial Intelligence (AI) perform the data processing, making the improvement observable (AI). Two technologies have allowed for previously impossible endeavours, such as the identification and tracking of objects for traffic surveillance systems. The need for an effective algorithm to unearth concealed elements in images grows in tandem with the number of such features. Single-object detection in the urban vehicle dataset is handled by a Convolutional Neural Network (CNN) model, while multi-object detection in the COCO and KITTI datasets is handled by YOLOv3. Metrics are used to evaluate and chart the performance of the models (mAP). On traffic surveillance video, we use YOLOv3 and SORT to follow objects as they move between frames. This study argues that cutting-edge networks like DarkNet deserve to be treated as special cases. We see effective detection and tracking on a dataset of urban vehicles. The algorithms produce very reliable identifications that can be used in real time for traffic applications.