Background subtraction is one of the techniques used in video surveillance system for detecting moving objects in a video. In this paper we propose a background subtraction method which gives output even when the camera is shaking.There are many challenges that we have to consider for developing a robust background subtraction method mainly illumination variation. Here the algorithm works in such a way that the input frames are compared and compensated with reference frame then separating the foreground object with respect to background. Background subtraction can be built very efficiently on an FPGA which can process 640x480 video sequence. Experimental result shows that it is possible to detect the moving object in a very fast manner. It is implemented in Digilent Atlys Spartan-6 FPGA development board can solve various problems like complex computation, data transmission, cost of hardware resources etc. The real time video is captured by using VmodCAM.

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IoT based Patient monitoring is the current need of the hour. The critical care services have been a crucial part for patients, who suffer from serious health conditions and heart diseases. Increased population is one of the major reasons behind waiting for clinical health monitoring services in hospitals. The current internet era and IoT based health support systems provide medical services for individuals and hospitals to forecast or early-detect probable critical care situations. The proposed approach is a new health care support system for heart patients to monitor and report with hospitals. We have introduced a Fog assisted IoT based cardio monitoring application to help the decision support system, which also tested with the existing analogues health care applications to show its elevated performance.

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A methodology is proposed by combining the application of markers along with watershed transformation and thresholding for image segmentation. Use of the traditional watershed algorithm is widespread because of its advantage of being able to produce a complete division of the image. However, its drawbacks include over-segmentation and noise sensitivity. Therefore, the marker-based watershed segmentation is proposed here to overcome these effects. First, the original image is preprocessed by filtering techniques in order to smoothen it. Secondly, the foreground objects are marked. Then, the background markers are computed. Finally, the marked image is transformed through watershed transformation. The area is computed for the segmented objects in the image. It has been proved that this method reduces the error percentage.

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