基于Bags of Gestuarts的3D Skeleton数据的线性时间在线动作检测
Linear-time Online Action Detection From 3D Skeletal Data Using Bags of Gesturelets
摘要：
滑动窗口是一种直接的方法来扩展一个成功的识别系统，以处理更具挑战性的检测问题。虽然动作识别仅决定动作是否存在于预分段视频序列中，但动作检测识别动作在未分段视频流中发生的时间间隔。然而，用于动作检测的滑动窗口方法可能很慢，因为它们在所有可能的子间隔上最大化分类器得分。即使新的方案利用动态规划来加速对最佳子间隔的搜索，它们也需要对整个视频序列进行离线处理。在本文中，我们提出了一种新的方法，在线动作检测的基础上提取的三维骨架序列的深度数据。它在线性时间内识别具有最大分类器得分的子区间。此外，它是不变性的时间尺度的变化，是适合于实时应用的低延迟。
英文摘要：
Sliding window is one direct way to extend a successful recognition system to handle the more challenging detection problem. While action recognition decides only whether or not an action is present in a pre-segmented video sequence, action detection identifies the time interval where the action occurred in an unsegmented video stream. Sliding window approaches for action detection can however be slow as they maximize a classifier score over all possible sub-intervals. Even though new schemes utilize dynamic programming to speed up the search for the optimal sub-interval, they require offline processing on the whole video sequence. In this paper, we propose a novel approach for online action detection based on 3D skeleton sequences extracted from depth data. It identifies the sub-interval with the maximum classifier score in linear time. Furthermore, it is invariant to temporal scale variations and is suitable for real-time applications with low latency.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
