Comparing Background Subtraction Algorithms and Method of Car Counting

In this paper, we compare various image background subtraction algorithms with the ground truth of cars counted. We have given a sample of thousand images, which are the snap shots of current traffic as records at various intersections and highways. We have also counted an approximate number of cars that are visible in these images. In order to ascertain the accuracy of algorithms to be used for the processing of million images, we compare them on many metrics that includes (i) Scalability (ii) Accuracy (iii) Processing time.

[1]  W. Eric L. Grimson,et al.  Adaptive background mixture models for real-time tracking , 1999, Proceedings. 1999 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (Cat. No PR00149).

[2]  ShahMubarak,et al.  Bayesian Modeling of Dynamic Scenes for Object Detection , 2005 .

[3]  Hélène Laurent,et al.  Review and evaluation of commonly-implemented background subtraction algorithms , 2008, 2008 19th International Conference on Pattern Recognition.

[4]  Yaser Sheikh,et al.  Bayesian modeling of dynamic scenes for object detection , 2005, IEEE Transactions on Pattern Analysis and Machine Intelligence.