一次性快速检测视频镜头边界、采样结构与动态关键帧。
Faster than real-time detection of shot boundaries, sampling structure and dynamic keyframes in video
- 结合帧间与帧内运动场和归一化互相关计算，统一处理三类视频分析任务。
- 算法运行速度达实时四倍以上，依赖稀疏选择性计算减少开销。
- 对大运动、闪光、闪烁等复杂内容表现鲁棒，适合实际视频处理场景。
镜头边界（硬切与短淡入淡出）、采样结构（渐进式/隔行/拉平）以及动态关键帧的检测是视频分析的基础步骤，需在开展高级分析前完成。本文提出一种新算法，通过结合运动场及归一化互相关得到的帧间与帧内度量，统一实现上述三项任务。由于采用稀疏且选择性地计算这些度量，算法运行速度达到实时的四倍以上。初步评估表明，该算法在存在大范围摄像机或物体运动、闪光、闪烁、低对比度或噪声等挑战性内容下仍具有极强鲁棒性。
原文摘要 · Abstract (English)
The detection of shot boundaries (hardcuts and short dissolves), sampling structure (progressive / interlaced / pulldown) and dynamic keyframes in a video are fundamental video analysis tasks which have to be done before any further high-level analysis tasks. We present a novel algorithm which does all these analysis tasks in an unified way, by utilizing a combination of inter-frame and intra-frame measures derived from the motion field and normalized cross correlation. The algorithm runs four times faster than real-time due to sparse and selective calculation of these measures. An initial evaluation furthermore shows that the proposed algorithm is extremely robust even for challenging content showing large camera or object motion, flashlights, flicker or low contrast / noise.
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