An algorithm to estimate mean vehicle speed from MPEG Skycam video
With low computation cost, motion vectors can be readily extracted from MPEG video streams and processed to estimate vehicle motion speed. A statistical model is proposed to model vehicle speed and noise. In order to achieve high estimation accuracy and also study the limitations of the proposed algorithm, we quantitatively evaluated four parameters used in our algorithm: temporal filter window size T, video resolution R-v (CIF/QCIF), motion vector frame distance m, and video bit-rates. Our experiments showed that the mean vehicle speed can be estimated with high accuracy, up to 85 to 92% by proper spatial and temporal processing. The proposed algorithm is especially suitable for Skycam-based application, where the traditional tracking-based or virtual-loop-based approaches perform poorly because of their requirements of high-resolution images. Although extensive work has been done in extracting motion information directly from MPEG video data in compressed domain, to our best knowledge, this paper is the very first work in which stationary motion (speed) of moving objects can be estimated with high accuracy directly from MPEG motion vectors. Furthermore the proposed method is not limited to vehicle speed estimation by nature and it can be applied to other applications where the stationary motion assumption is satisfied.
mean vehicle speed mpeg skycam motion vectors