Monocular vision-based vehicular speed estimation from compressed video streams

This paper introduces a monocular vision-based vehicular speed estimation algorithm that operates in the compressed domain. The algorithm relies on the use of motion vectors associated with video compression to achieve computationally efficient and accurate speed estimation. Building the speed estimation directly into the compression step adds only a small amount of computation which is conducive to real-time performance. We demonstrate the effectiveness of the algorithm on 30 fps video of one hundred and forty vehicles travelling at speeds ranging from 30 to 60 mph. The average speed estimation accuracy of our algorithm across the test set was better than 2.50% at a yield of 100%, with the accuracy increasing as the yield decreases and as the frame rate increases.