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Online Kernel Slow Feature Analysis for Temporal Video Segmentation and Tracking

机译:用于时间视频分割和跟踪的在线内核慢特征分析

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摘要

Slow feature analysis (SFA) is a dimensionality reduction technique which has been linked to how visual brain cells work. In recent years, the SFA was adopted for computer vision tasks. In this paper, we propose an exact kernel SFA (KSFA) framework for positive definite and indefinite kernels in Krein space. We then formulate an online KSFA which employs a reduced set expansion. Finally, by utilizing a special kind of kernel family, we formulate exact online KSFA for which no reduced set is required. We apply the proposed system to develop a SFA-based change detection algorithm for stream data. This framework is employed for temporal video segmentation and tracking. We test our setup on synthetic and real data streams. When combined with an online learning tracking system, the proposed change detection approach improves upon tracking setups that do not utilize change detection.
机译:慢特征分析(SFA)是一种降维技术,已与视觉脑细胞的工作方式联系在一起。近年来,SFA被用于计算机视觉任务。在本文中,我们为Kerin空间中的正定和不定核提出了一个精确的核SFA(KSFA)框架。然后,我们制定了一个在线KSFA,它使用了减少的集合扩展。最后,通过使用一种特殊的内核系列,我们可以制定确切的在线KSFA,而无需缩减集。我们应用提出的系统来开发基于SFA的流数据变化检测算法。该框架用于时间视频分割和跟踪。我们在合成和真实数据流上测试设置。当与在线学习跟踪系统结合使用时,所提出的变更检测方法将改进不利用变更检测的跟踪设置。

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