Research on vehicle video image stabilization algorithm for mine

Abstract

For high error matching rate and poor real-time performance of global motion vector estimation caused by foreground moving target and high noise in video image captured by mine vehicle camera system, a vehicle video image stabilization algorithm for mine was proposed which was based on ORB feature matching and improved particle filter. In motion vector estimation stage, ORB algorithm is used to complete extraction of image feature points firstly. Then continuous three-frame difference method based on image block is adopted to quickly mark foreground movement area jointing space-time consistency criteria. Combined with foreground mark area, location of feature points is selected for the first time, and retained points are registered. Finally, affine transformation model is used to estimate motion vectors among frames. In motion filtering stage, a real-time particle filter algorithm based on estimation window is used to filter jitter components, and compensation parameters are obtained. The experimental results show that the proposed algorithm can effectively avoid influence of foreground moving target on image stabilization precision, and has quick processing speed.

Keywords

• mine video monitoring
• vehicle video
• image stabilization
• motion vector estimation
• orb feature matching
• particle filter