TrafficDetection

DESCRIPTION: We implement a system for vehicle detection and tracking from traffic video using Gaussian mixture models and Bayesian estimation. In particular, the system provides robust foreground segmentation of moving vehicles through a K-means clustering approximation as well as vehicle tracking correspon- dence between frames by correlating Kalman and particle filter prediction updates to current observations through the solution of the assignment problem. In addition, we conduct performance and accuracy benchmarks that show about a 90 percent reduc- tion in runtime at the expense of reducing the robustness of the mixture model classification and about a 30 percent and 45 percent reduction in accumulated error of the Kalman filter and particle filter respectively as compared to a system without any prediction.

*In retrospect, the results for the particle filter were found to be miscalculated. Adjustment of the model assumptions must be made to show particle filter tracking improvements.

Read the project report for a more detailed description of the code.

INSTRUCTIONS: In order to run the code,

run the following command

foregroundEstimation('justCarsTrim.avi');

or any other .avi video you wish to see the results of.

This will output a video called 'foreground.avi' which will show the background segmentation

and tracking applied contingent upon whether

trackingOn = 1;

or

trackingOn = 0;

Be warned as this takes about 30 minutes to run the simulation and generate the file.

However, if you'd like you can view the result at the youtube link below instead which shows both output results. https://www.youtube.com/watch?v=GwyQ3QdBzaY