How to Estimate Speed with Computer Vision
Blog post from Roboflow
In the blog post, Piotr Skalski presents a comprehensive guide on estimating vehicle speed using computer vision techniques, detailing the process from object detection to speed calculation. The tutorial begins with object detection using models like the yolov8x-640 from Roboflow, facilitated by libraries such as Supervision and Inference, and discusses the integration of multi-object tracking using BYTETrack to maintain vehicle tracking across frames. A significant challenge addressed is perspective distortion, which affects distance measurement due to camera angles, and is resolved using OpenCV's perspective transformation functions. The post outlines the creation of a transformation matrix to convert image coordinates into real-world measurements, and discusses the importance of averaging speed calculations over time to account for fluctuations caused by detection inconsistencies. Skalski also highlights complexities such as occlusion and road slope, offering insights into refining speed estimation systems in real-world applications, and concludes with the potential for further innovation in this area.