Human Action Recognition Using Adaptive Local Motion Descriptor in Spark

Human action recognition plays a significant part in the computer vision and multimedia research society due to its numerous applications. However, despite different approaches proposed to address this problem, some issues regarding the robustness and efficiency of the action recognition still need to be solved. Moreover, due to the speedy development of multimedia applications from numerous origins, e.g., CCTV or video surveillance, there is an increasing demand for parallel processing of the large-scale video data. In this paper, we introduce a novel approach to recognize the human actions. First, we explore Apache spark with in-memory computing, to resolve the task of human action recognition in the distributed environment. Secondly, we introduce a novel feature descriptor, namely, adaptive local motion descriptor (ALMD) by considering motion and appearance, which is an extension of local ternary pattern used for static texture analysis, and ALMD also generate persistent codes to describe the local-textures. Finally, the spark machine learning library random forest is employed to recognize the human actions. Experimental results show the superiority of the proposed approach over other state-of-the-arts.