iosr-JCE   Volume-2 ~ Issue-4

Abstract: This paper discusses an efficient and effective approach for identifying and tracking of moving object from a video. A video is captured by stationary camera. Moving object tracking and detection from video sequences has applications in several areas such as automatic video surveillance, motion-based recognition, video indexing, human-computer interaction, traffic monitoring, and vehicle navigation. In this work, we present a computer vision-based approach for object tracking and detection. A method is proposed to detect and track moving object through video even if background is changed at any instant and capable of plotting a 3D graph mesh based on the moving object in between any number of frames per second. We use consecutive frame analysis technique to detect background changing criteria and use morphological filtering for image enhancement. Finally, we will get the co-ordinates for the moving object and these co-ordinates are imported to any other 3D software's like MAYA etc to analyze or edit the results calculated by the algorithm.
Keywords- Object tracking, Object detection, Motion estimation, Computer vision.

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[9] Mohammed Sayed and Wael Badawy "A novel motion estimation method for mesh- based video motion tracking", IEEE, pp.337-340, 2004.
[10] Li-Qun Xu "Simultaneous Tracking And Segmentation Of Two Free Moving Hands In A Video Conferencing Scenario", IEEE, pp.49-52, 2003...................

Abstract: Wireless Sensor Networks (WSN) consists of distributed sensor devices. Healthcare application domain is one of the emerging domains in the current world. Nowadays WSN is more popular in healthcare applications and it produces enormous amount of data in a periodic interval. The data should be effectively stored and later it should be processed and analyzed by the doctors to understand the health conditions of the patients. But the main drawback of WSN is it could not able to store large amount of data. Hence there is a need for the scalable environments like Grid to effectively store the data and use later for processing and analyzing the data. To accomplish the objective, in this research paper we have investigated to integrate WSN with Grid environments using the resource broker based approach. The proposed work is integrated with WSN using the Proxy Connector to collect the healthcare data. The collected data is parsed and allocated to the Grid resources using Genetic Algorithm (GA) based scheduling mechanism. The proposed work is aimed to decrease the data transfer time and increase the success rate of data job requests and throughput.
Keywords: Grid Computing, Wireless Sensor Networks (WSN), Genetic Algorithm, Healthcare applications, Scheduling.

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Abstract: Software development effort and cost prediction is one of the important activities in software project management. Accuracy in prediction is a challenge for software developers. There are many models exists that defines a relationship between size and effort. Cost of developing a project increases with complexity of project accuracy predictions are strongly required during the early stages of project development. Because data and info available at the starting phases of project is not complete, not consistent and not even certain. An objective of the software engineering community is to develop a useful model that define the development life-cycle and accurately predict the cost of developing a software product. In this paper we discuss Neuro-Fuzzy model deals with this situation. Neuro-Fuzzy models are the combination of Artificial Neural Network and Fuzzy Logic. Artificial Neural Network has the ability to learn from previous data. It model complex relationships between both independent variables (cost drivers) and dependent variables (effort). Fuzzy logic simulates the human behavior and reasoning. Fussy logic is basically used in situation where decision making is very difficult and conditions are not clearly defined. Facts that may be dismissed are focused in this technique.
Keywords: Neural Network, Fuzzy Logic, Artificial Neural Network.

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