Car-speed-detection in python using object detection

By Datla Krishna Karthik varma

Python-based object recognition method for detecting autos and measuring their speeds in video frames is used for a variety of applications.

Using Python object identification techniques, the Car Speed identification project seeks to gauge the speed of moving objects. To calculate a car's speed for use in various applications, such as traffic monitoring, surveillance systems, and intelligent transportation management, it entails processing video frames to find and track cars.

The project uses an object identification model that has already been trained, such as YOLO (You Only Look Once) or Faster R-CNN (Region-based Convolutional Neural Networks). Large datasets are used to train these models to identify items in pictures or video frames, including autos.

Extracting frames from a video source, such as a security camera or previously recorded video, is the first stage. The object detection model is then used to each frame in order to recognise and pinpoint any autos that may be there. The steps in this technique include pixel analysis, object detection using their features, and constructing bounding boxes around the recognised cars.

The next stage is to follow the automobiles' motion over successive frames after they have been identified and their bounding boxes have been determined. Several techniques, including the optical flow algorithm and the Kalman filter, can be used for tracking. Accurate speed estimations are made possible thanks to these algorithms, which assess the car's position and velocity.

The distance the car travels between two successive frames is measured to determine its speed. This can be done by using either the scale factor obtained through calibration procedures or the known distance between two reference sites inside the scene. The speed of the car is calculated by dividing the distance by the interval of time between the frames.

Finally, there are numerous applications for the speed data. For instance, the data can be utilised in traffic monitoring systems to spot speed traps, spot congested locations, or improve traffic flow. It can aid in spotting questionable or careless driving behaviours in surveillance systems.