The primary objective is to identify and segments the multiple, partly occluded objects in the image. The subsequent stage carry out our approach, primarily start with frame conversion. Next in the preprocessing stage, the Gaussian filter is employed for image smoothening. Then from the preprocessed image, Multi objects are segmented through modified ontology-based segmentation, and the edge is detected from the segmented images. After that, from the edge detected frames area is extracted, which results in object detected frames. In the feature extraction stage, attributes such as area, contrast, correlation, energy, homogeneity, color, perimeter, circularity are extorted from the detected objects. The objects are categorized as human or other objects (bat/ball) through the feed-forward back propagation neural network classifier (FFBNN) based upon the extracted attributes.

The evolution in computing strategies has shown wonders in reducing the reachability issue among different end devices. After centralized approaches, decentralized approaches started to take action, but with the latency in data pre-processing, computing very simple requests was the same as for the larger computations. Now it's time to have a simple decentralized environment called edge that is created very near to the end device. This makes edge location friendly and time friendly to different kinds of devices like smart, sensor, grid, etc. In this chapter, some of the serious and non-discussed security issues and privacy issues available on edge are explained neatly, and for a few of the problems, some solutions are also recommended. At last, a separate case study of edge computing challenges in healthcare is also explored, and solutions to those issues concerning that domain are shown.

The evolution in computing strategies has shown wonders in reducing the reachability issue among different end devices. After centralized approaches, decentralized approaches started to take action, but with the latency in data pre-processing, computing very simple requests was the same as for the larger computations. Now it's time to have a simple decentralized environment called edge that is created very near to the end device. This makes edge location friendly and time friendly to different kinds of devices like smart, sensor, grid, etc. In this chapter, some of the serious and non-discussed security issues and privacy issues available on edge are explained neatly, and for a few of the problems, some solutions are also recommended. At last, a separate case study of edge computing challenges in healthcare is also explored, and solutions to those issues concerning that domain are shown.

The evolution in computing strategies has shown wonders in reducing the reachability issue among different end devices. After centralized approaches, decentralized approaches started to take action, but with the latency in data pre-processing, computing very simple requests was the same as for the larger computations. Now it's time to have a simple decentralized environment called edge that is created very near to the end device. This makes edge location friendly and time friendly to different kinds of devices like smart, sensor, grid, etc. In this chapter, some of the serious and non-discussed security issues and privacy issues available on edge are explained neatly, and for a few of the problems, some solutions are also recommended. At last, a separate case study of edge computing challenges in healthcare is also explored, and solutions to those issues concerning that domain are shown.