Large-scale human action recognition with spark

In this paper, Apache Spark, the rising big data processing tool with in-memory computing ability, is explored to address the task of large-scale human action recognition. To achieve this, several advanced key techniques for human action recognition, such as trajectory based feature extraction, Gaussian Mixture Model, Fisher Vector, etc., are realized with parallel distributed computing power on Spark. The theory and implementation details for these distributed applications are presented in this work. The experimental results on the benchmark human action dataset Hollywood-2 show that the proposed Spark based framework which is deployed on a 9-node computer cluster can deal with large-scale video data and can dramatically accelerate the process of human action recognition.