{"id":16882,"date":"2024-08-23T05:23:30","date_gmt":"2024-08-23T05:23:30","guid":{"rendered":"https:\/\/codewithcurious.com\/?p=16882"},"modified":"2024-09-27T10:57:16","modified_gmt":"2024-09-27T10:57:16","slug":"vehicle-speed-estimation-using-python","status":"publish","type":"post","link":"https:\/\/codewithcurious.com\/projects\/vehicle-speed-estimation-using-python\/","title":{"rendered":"Vehicle Speed Estimation Using Open CV Python With Source Code"},"content":{"rendered":"\t\t
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Vehicle Speed Estimation Using Open CV Python With Source Code<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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Introduction :<\/h3>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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This project uses the optical flow algorithm, specifically the Lucas-Kanade tracker, to estimate vehicle speeds from mono camera (CCTV) footage. Speed of a vehicle is an important parameter in many application areas such as traffic management, law enforcement and autonomous vehicles. It is being used for road safety monitor, speed control enforcement actions and increased efficiency of transportation systems due to accurate estimation. Now we can calculate the speed of a vehicle by using computer vision and machine learning. Here we are going to learn how to estimate the speed of a car and for that, I will be using Computer Vision powerful library OpenCV in Python. OpenCV is a complete open- source platform that has useful tools related to processing video and image, which makes it an excellent choice for implementing speed estimation systems.<\/p>

Language And Interface :-<\/h5>

Vehicle Speed Estimation Using Python , A Versatile Programming Language With Source Code<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t

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Required Modules Or Packages:-<\/h3>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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You Need The Packages List Given Below :-<\/p>\n

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  1. sys<\/strong> :- System-specific parameters and functions sys This module provides access to some variables used or maintained by the interpreter .<\/li>\n
  2. _future_<\/strong> :- gives access to features from future python versions in the current interpreter, permitting forward compatibility.<\/li>\n
  3. Functools<\/strong> :- Implements higher-order functions for working with functions, such as decorators and other tools to build fault-tolerant APIs.<\/li>\n
  4. Numpy<\/strong> :- It is a core library for numerical computation in python and it can handle large arrays of numbers.<\/li>\n
  5. cv2<\/strong> :- It is a library to use in Computer Vision You will get basic image processing tools while using cv2 for example, Image Reading and Writing .<\/li>\n
  6. Os<\/strong> :- It offers facilities for working with OS, such as handling files and<\/li>\n
  7. Itertools<\/strong> :-This module implements a number of iterator building blocks inspired by constructs from APL, Haskell.<\/li>\n
  8. Contextlib<\/strong> :- some of the utilities I have found useful when working with context managers and code that need to close up resources, used in combining multiple operations together which requires being executed in one section killed.<\/li>\n
  9. Common<\/strong> :- Often just a shared module or package, but not including<\/li>\n
  10. Video<\/strong> :- Likely a custom or implementation specific video processing module in your project and not one from the standard libraries.<\/li>\n
  11. tst_scene_render<\/strong> :- Most likely a package or custom module for scene rendering in some form of test environment, not part of the standard library.<\/li><\/ol>