Coronavirus has significantly entered the globe and has affected everyone's life in some form or another. The identification of the coronavirus is as important as prevention. An early and precise diagnosis will have significant-good benefits for us. The Convolutional Neural Network can be implemented for virus detection, and content-adaptive progressive occlusion analysis could be used to handle occlusion. To improve precision, it is also possible to add many other classification methods accordingly, which are mentioned in this paper.
Background subtraction is a key part to detect moving objects from the video in computer vision field. It is used to subtract reference frame to every new frame of video scenes. There are wide varieties of background subtraction techniques available in literature to solve real life applications like crowd analysis, human activity tracking system, traffic analysis and many more. Moreover, there were not enough benchmark datasets available which can solve all the challenges of subtraction techniques for object detection. Thus challenges were found in terms of dynamic background, illumination changes, shadow appearance, occlusion and object speed. In this perspective, we have tried to provide exhaustive literature survey on background subtraction techniques for video surveillance applications to solve these challenges in real situations. Additionally, we have surveyed eight benchmark video datasets here namely Wallflower, BMC, PET, IBM, CAVIAR, CD.Net, SABS and RGB-D along with their available ground truth. This study evaluates the performance of five background subtraction methods using performance parameters such as specificity, sensitivity, FNR, PWC and F-Score in order to identify an accurate and efficient method for detecting moving objects in less computational time.
Facial expression identifies the basic human emotions. It helps to tell the person by watching the image whether the person is actually telling the truth about what he is claiming or not. Human emotions detection is used in human computer interaction, military, law-enforcements, for autistic people and robotics etc. No one thought that detecting human emotions would be possible for a machine but computer science changed all that. Various techniques like Bezier curve, longest binary pattern and various classifiers have been implemented till now in this field. This paper will review the research work carried out in this field of human emotion detection from still images.
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