alnaeini / carspeed

Speed of a car is predicted from Dash Cam videos with fast training and less than 3% error using linear regression and optical flow.

DashCamp_CarSpeed

Introduction

This repos is an attempt to address the commaai challenge. The challenge is to predict the car speed with recorded videos from Dash Cam located under the mirror in front of a car. Check this link for more details about the challenge and downloading the data: speedchallenge.

Optical Flow

Based on previous attempts, one of the fastest approach in terms of training and performance is to apply the Optical flow concept.

Optical flow is one of the most basic concepts in computer vision, and refers to the apparent motion of objects in the image due to the relative motion between camera and scene.

Using Optical flow, we can calculate the two components of speed(u,v) using the following equations:

where V and w are the linear and angular velocities of the camera and h is the distance between the camera and the plane(road).

In this approach the angular velocity is neglected. for more details, can take a look at this blog and its references:Car speed estimation from a windshild camera.

Performance

The train dataset(train.mp4) is divided into the data into train(95%) and validation(5%). The mean squared error for train and validation are as follows:

• Training dataset - 4.7
• Validation dataset - 2.66

Plot

The training graph:

The valiation graph:

Test speed prediction(the ground truth is not released publicly)

The speed prediction for test video(test.mp4) is generated and saved in this repo as test.txt.

Running the code

First, download the data folder from speedchallenge. Then just run the speed_challenge.py (make sure tools.py and data folder are available in the same directory).

Future Improvement

The model does not currently predict the car's speed accurately where there is no lane lines. This issue is most obvious in the testset and should be addressed in the future improvements.