Action recognition in videos is possible using edge detection techniques such as Cany edge detection algorithm and Sobel algorithm. The surveillance videos are the major complex criterion for analyzing and produce the valuable results such as face recognition and action recognition. But analysis of such videos is necessary for the real-time applications like safety and security systems. To increase the scalability of the edge detection based action recognition algorithm, the features of the video is grouped together using Hadoop Image Processing Interface (HIPI). MapReduce algorithm is proposed to parallelize the action recognition algorithm to achieve the best results in large scale videos in minimum amount of time. The video has to be converted to image and group of images and assembled as HIPI image bundle for analyzing the video effectively. © IAEME Publication