基于ViBe算法运动特征的关键帧提取算法

针对视频关键帧提取算法中运动类视频运动目标特征不易提取所造成的错选和漏选问题,提出一种基于背景建模(visual background extractor, ViBe)算法的前景运动目标特征提取的关键帧提取算法。通过ViBe算法对视频序列进行前景目标检测,提取前景运动目标的尺度不变特征变换(scale invariant feature transform, SIFT)特征,并对相邻帧之间的特征数据进行特征点匹配,根据定义的公式计算视频帧的相似度,然后根据提出的关键帧判别方法输出视频的关键帧。试验结果表明,该算法能较好的解决运动类视频关键帧提取中出现的漏选和错选问题,与基于SIFT分布直方图的算法相比,其查准率和查全率的综合指标F₁值有较好提高。因此该算法对于判别运动类视频中包含关键动作的关键帧具有较好的检测效果。

Key frame extraction based on ViBe algorithm for motion feature extraction

Aiming at the fact that the background was dominant in the key frame extraction algorithm, in which the foreground target was too small and it was not easy to extract the features of moving targets in sports video, a key frame extraction algorithm for foreground moving target feature extraction based on background modeling algorithm was proposed, which was called visual background extractor (ViBe) algoritm. The foreground target detection of video sequence was firstly carried out using ViBe algorithm, afterwards the scale-invariant feature transformation (SIFT) features of the foreground moving target were extracted. Based on the similarity calculated from video frame series, the key frames of video were output according to the key frame discrimination method. The experimental results showed that the proposed algorithm could solve the problem of missed selection and misselection in traditional key frame extraction. Compared with the algorithm based on SIFT distribution histogram, the F₁ score was well improved. The algorithm based on ViBe could effectively identify key frames in sports video.