融入时空显著性的高精度视频稳像算法

High-accuracy video image stabilization algorithm incorporating temporal and spatial saliency

为剔除复杂运动前景对视频稳像精度的干扰,同时结合时空显著性在运动目标检测上的独特优势,提出一种融入时空显著性的高精度视频稳像算法。该算法一方面通过时空显著性检测技术识别出运动目标并对其进行剔除;另一方面,采用多网格的运动路径进行运动补偿。具体包括:SURF特征点提取和匹配、时空显著性目标检测、网格划分与运动矢量计算、运动轨迹生成、多路径平滑、运动补偿等环节。实验结果表明,相较于传统的稳像算法,所提算法在稳定度(Stability)指标方面表现突出。对于有大范围运动前景干扰的视频,所提算法比RTVSM(Robust Traffic Video Stabilization Method assisted by foreground feature trajectories)的Stability指标提高了约9.6%;对于有多运动前景干扰的视频,所提算法比Bundled-paths算法的Stability指标提高了约5.8%,充分说明了所提算法对于复杂场景的稳像优势。

In order to eliminate the interference of complicated moving foreground on the accuracy of video stabilization,and to combine with the unique advantages of temporal and spatial saliency in moving target detection,a high-accuracy video stabilization algorithm incorporating temporal and spatial saliency was proposed.In the proposed algorithm,the spatio-temporal saliency detection technology was used to identify and eliminate the moving targets.At the same time,the multi-grid motion paths were adopted for motion compensation.The proposed algorithm specifically includes:Speeded Up Robust Features(SURF)feature point extraction and matching,spatio-temporal saliency target detection,grid division and motion vector calculation,motion trajectory generation,multi-path smoothing,motion compensation and so on.Experimental results show that compared with traditional image stabilization algorithms,the proposed algorithm has outstanding performance in stability index.For videos with moving large range of foreground interference,the stability of the proposed algorithm is improved by about 9.6%compared with Robust Traffic Video Stabilization Method assisted by foreground feature trajectories(RTVSM).For videos with multiple moving foreground interference,the stability of the proposed algorithm is improved by about 5.8%compared with Bundled-paths algorithm,which fully verify the proposed algorithm’s image stability advantage.