Compressive sensing based velocity estimation in video data
This addresses the problem of efficient velocity estimation for traffic monitoring or surveillance applications, but it appears incremental as it combines existing compressive sensing and time-frequency analysis methods.
The paper tackled velocity estimation of moving vehicles from video data using compressive sensing and sparse reconstruction algorithms, achieving accurate velocity estimation even with a very reduced number of video frames.
This paper considers the use of compressive sensing based algorithms for velocity estimation of moving vehicles. The procedure is based on sparse reconstruction algorithms combined with time-frequency analysis applied to video data. This algorithm provides an accurate estimation of object's velocity even in the case of a very reduced number of available video frames. The influence of crucial parameters is analysed for different types of moving vehicles.