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【24h】Distributed Feature Extraction on Apache Spark for Human Action Recognition

机译Apache Spark上的分布式特征提取,用于人类动作识别

【摘要】 Local feature extraction is one of the most important tasks to build robust video representation in human action recognition. Recent advances in computing visual features, especially deep-learned features, have achieved excellent performance on a variety of action datasets. However, the extraction process is computing-intensive and extremely time-consuming when conducting it on large-scale video data. Consequently, to extract video features over big data, most of the existing methods that run on single machine become inefficient due to the limit of computation power and memory capacity. In this paper, we propose the elastic solutions for feature extraction based on the Spark framework. Particularly, exploiting the in-memory computing capability of Spark, the process of computing features are parallelized by partitioning video data into videos or frames and place them into resilient distributed datasets (RDDs) for the subsequent processing. Then, we present the parallel algorithms to extract the state-of-the-art deep-learned features on the Spark cluster. Subsequently, using the distributed encoding, the extracted features are aggregated into the global representation which is fed into the learned classifier to recognize actions in videos. Experimental results on a benchmark dataset demonstrate that our proposed methods can significantly speed up the extraction process and achieve the promising scalability performance.

【摘要机译】局部特征提取是在人类动作识别中建立可靠的视频表示的最重要任务之一。在计算视觉特征(尤其是深度学习的特征)方面的最新进展,已在各种动作数据集上实现了出色的性能。但是,提取过程在大规模视频数据上进行时,需要大量的计算并且非常耗时。因此,要在大数据上提取视频特征,由于计算能力和存储容量的限制,大多数在单机上运行的现有方法效率很低。在本文中,我们提出了基于Spark框架的特征提取弹性解决方案。特别是,利用Spark的内存中计算功能,通过将视频数据划分为视频或帧并将它们放入弹性分布式数据集(RDD)中进行并行处理,可以并行处理计算功能的过程。然后,我们提出并行算法以提取Spark集群上最新的深度学习功能。随后,使用分布式编码,将提取的特征聚合到全局表示中,然后将其馈入学习的分类器中以识别视频中的动作。在基准数据集上的实验结果表明,我们提出的方法可以显着加快提取过程并实现有希望的可伸缩性性能。

【作者】Nguyen Anh Tu; Thien Huynh-The; Kok-Seng Wong; Dinh-Mao Bui; Young-Koo Lee;

【作者单位】Nazarbayev University Dept. Computer Science Nur-Sultan Republic of Kazakhstan; Kumoh National Institute of Technology ICT Convergence Research Center Gumi South Korea; Nazarbaye;

【年(卷),期】2020(),

【年度】2020

【页码】1-6

【总页数】6

【正文语种】

【中图分类】;

【关键词】Feature extraction; Sparks; Trajectory; Task analysis; Liquid crystal displays; Visualization; Streaming media;

机译 特征提取;火花;弹道;任务分析;液晶显示器;可视化;流媒体;
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