Speed Estimation Tutorial: Measuring Motion Like a Pro

Introduction

In the world of computer vision, accurately estimating the speed of moving objects is a crucial task with applications in traffic monitoring, autonomous driving, sports analysis, and more. However, achieving precise speed measurements is not as straightforward as it might seem. Various challenges, such as object detection, tracking, and perspective distortion, can significantly affect the accuracy of your results.

This tutorial will guide you through a comprehensive approach to speed estimation using state-of-the-art tools: YOLOv8 for vehicle detection and ByteTrack for multi-object tracking. Additionally, we'll tackle the often-overlooked issue of perspective distortion—a phenomenon where objects appear differently based on their distance from the camera, leading to distorted speed measurements.

By the end of this tutorial, you'll not only understand how to detect and track vehicles in video footage but also how to correct for perspective distortion to achieve accurate, real-world speed estimates. Whether you're working on a research project, developing a commercial application, or just exploring the fascinating field of computer vision, this guide will equip you with the skills and knowledge needed to measure motion like a pro.

Let's dive in and explore the cutting-edge techniques that make reliable speed estimation possible!

What is Speed Estimation?

Before we jump into coding, let’s break down the concept. Speed estimation involves calculating the velocity of an object in motion. It’s determined by measuring the distance traveled by the object over time.

Imagine tracking a car on the road or a player on the soccer field—speed estimation allows you to quantify how fast they’re moving.

Step 1: Setup Your Environment

First things first, let’s set up the environment. You’ll need Python, OpenCV, and some video footage to work with. If you haven’t installed OpenCV yet, you can do so using pip:

pip install opencv-python supervision ultralytics

Step 1.2: Read and Write video

import cv2

# Video capture and settings
cap = cv2.VideoCapture("input.mp4")
assert cap.isOpened(), "Error reading video file"
w, h, fps = (int(cap.get(x)) for x in (cv2.CAP_PROP_FRAME_WIDTH, cv2.CAP_PROP_FRAME_HEIGHT, cv2.CAP_PROP_FPS))

# Video writer setup
video_writer = cv2.VideoWriter("output.mp4", cv2.VideoWriter_fourcc(*"mp4v"), fps, (w, h))
...

Step 2: Object detection

In this step, we will use YOLOv8, a state-of-the-art model known for its exceptional performance in real-time object detection tasks, including vehicle detection.

1. Download and Load YOLOv8 Model Weights

from ultralytics import YOLO

# Initialize YOLOv8 model
model = YOLO("yolov8x.pt")