A Design of Hybrid Workflow Model for Real Time Object Detection using Temporal Frame Differencing Algorithm: A Cloud Computing Approach M. Nayagam Gomathy*, Dr. Ramar K.** *Research Scholar, Anna University, Chennai, Tamilnadu, India **Professor, Department of CSE, Einstein College of Engineering, Tirunelveli, Tamilnadu, India Online published on 14 October, 2016. Abstract Today, Computer Vision is one of the most well-liked and critical research areas in the Computer Science field. Moving object detection is the first and foremost step in any computer vision applications. The detection of moving object in any computer vision application is a very critical task. The most widely used method for moving object detection is Frame differencing based background subtraction method. The most of the researchers address the various challenges of background subtraction algorithm to detect the object accurately, the segmentation of objects from non stationary objects is critical, adaptation of illumination changes and handling of camouflage and etc. But they are more CPU and I/O intensive one. Cloud Computing is the new buzz word in computer field which provides computational power on demand basis which requires the representation of e-science workflow model for such kind of large-scale scientific applications. There are four different types of scientific work flow models viz Montage, LIGO, SIPHT, and Cybershake are exist which are varied by either CPU intensive or I/O intensive application. But the Temporal frame differencing based real time object detection is both CPU and I/O intensive one. Hence which could not be expresses as a single workflow model for cloud computing. So, we proposed a hybrid workflow model (Montage and SIPHT) of temporal frame differencing algorithm for real time object detection and the performance of such workflow model is tested and analyzed in already existing scheduling algorithm in workflowsim. Top Keywords Moving Object Detection and Tracking, Temporal Frame differencing, Workflow Model and Cloud Computing. Top |