This paper presents a comprehensive analysis of motion vectors extracted from AV1-encoded video streams and their application in accelerating optical flow estimation. We demonstrate that motion vectors from AV1 video codec can serve as a high-quality and computationally efficient substitute for traditional optical flow, a critical but often resource-intensive component in many computer vision pipelines. Our primary contributions are twofold. First, we provide a detailed comparison of motion vectors from both AV1 and HEVC against ground-truth optical flow, establishing their fidelity. In particular we show the impact of encoder settings on motion estimation fidelity and make recommendations about the optimal settings. Second, we show that using these extracted AV1 motion vectors as a "warm-start" for a state-of-the-art deep learning-based optical flow method, RAFT, significantly reduces the time to convergence while achieving comparable accuracy. Specifically, we observe a four-fold speedup in computation time with only a minor trade- off in end-point error. These findings underscore the potential of reusing motion vectors from compressed video as a practical and efficient method for a wide range of motion-aware computer vision applications.

翻译：本文对从AV1编码视频流中提取的运动矢量及其在加速光流估计中的应用进行了全面分析。我们证明，AV1视频编解码器产生的运动矢量可作为传统光流的高质量、计算高效替代方案，而传统光流是众多计算机视觉流程中关键但通常计算密集的组成部分。我们的主要贡献包括两个方面。首先，我们详细比较了AV1与HEVC的运动矢量相对于真实光流的保真度，确立了其可靠性。特别地，我们分析了编码器设置对运动估计保真度的影响，并给出了优化设置建议。其次，我们证明将这些提取的AV1运动矢量作为先进深度学习光流方法RAFT的"热启动"输入，能在保持相当精度的同时显著缩短收敛时间。具体而言，我们观察到计算时间实现了四倍加速，而端点误差仅略有增加。这些发现凸显了复用压缩视频中运动矢量作为一种实用高效方法，在广泛运动感知计算机视觉应用中的潜力。