Traffic flow detection and statistics via improved optical flow and connected region analysis

Moving vehicle detection plays an important role in intelligent transportation systems. One of the common methods used in moving vehicle detection is optical flow. However, conventional Horn-Schunck optical flow consumes too much time when calculating dense optical flows so that it cannot meet the real-time requirements. This paper proposes a novel improved Horn-Schunck optical flow algorithm based on inter-frame differential method. In our algorithm, optical flow field distribution is only calculated for pixels with larger gray values in the difference image, while for other pixels we applied the iterative smooth. The number of vehicles in the videos of traffic conditions is counted by setting the virtual loop and detecting optical flow information. To extract the moving vehicle as accurately as possible, we also propose a method to obtain moving vehicle minimum bounding rectangle based on the connected region analysis. Finally, we compare the improved optical flow with other four optical flow algorithms in moving vehicle extraction and vehicle flow detection, from which our method gives a much more accurate result.