Vehicle Detection and Tracking plays a pivotal role in the realm of Traffic Management and Smart Cities, offering indispensable benefits. In the context of traffic management, this technology enables real-time monitoring of vehicular movement, helping authorities to optimize traffic flow, reduce congestion, and enhance overall road safety. For Smart Cities, vehicle detection provides the data necessary for intelligent transportation systems to function effectively. It enables the development of adaptive traffic signals, efficient public transportation, and data-driven policies that promote sustainability and a higher quality of life for urban residents. Moreover, this technology contributes to reducing energy consumption and emissions, making it a fundamental tool in the transition to more eco-friendly and sustainable urban environments. In essence, Vehicle Detection and Tracking are essential cornerstones for the transformation of modern cities into more efficient, connected, and livable hubs of human activity.
We'll learn how to use YoloV8 (Object Detection) and ByteTrack (Tracking) in order to detect and track vehicles on a crossroad. Each vehicle that are turning left, right or moving straight ahead will be counted. Nonetheless, these models only work for car detection and not for any other vehicle (bycicle, bus, etc.).
- A shell script, which allows you to download the input video and the pretrained weights for YOLO.
- A python script that will generate the output video
- A requirements text file that lists all the different versions from installed libraries.
This project was inspired by Roboflow's project on Vehicle Detection.