Comparing Background Subtraction Algorithms and Method of Car Counting
This work addresses the problem of efficient and accurate car counting from traffic images for traffic management applications, but it is incremental as it focuses on comparing existing methods.
The paper compared various background subtraction algorithms for car counting using a dataset of 1,000 traffic images, evaluating them on scalability, accuracy, and processing time to determine their suitability for processing millions of images.
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.