Chaos Game Optimization with Machine Learning Enabled Social Distance Detection and Classification Model
Keywords:
Social Distancing, Median Filtering, Machine Learning, Euclidean Distance, Image SegmentationAbstract
Social distancing (SD) links with an action that overcomes the spread of virus, by decreasing the physical contact of human beings such as gatherings at public locations such as parks, universities, schools, airports, malls, hospitals, offices, factories, etc. to avoid crowds and maintain a sufficient distance among people. SD is very vital mainly for people who are in high danger of severe infection from COVID-19. SD detection naturally denotes the usage of technical tools and techniques to observe and apply SD rules. In earlier years, machine learning (ML), deep learning (DL) and computer vision have displayed great outcomes for many daily life issues. In this view, this study presents a Chaos Game Optimization with ML Enabled Social Distance Detection and Classification (CGOML-SDDC) technique. The foremost aim of the CGOML-SDDC technique is to identify social distance between low-risk and high-risk groups. In the developed CGOML-SDDC model, the primary phase of data pre-processing has been performed in two ways: median filtering (MF) based noise elimination and Adaptive Histogram Equalization (AHE) based contrast enhancement. In addition, the CGOML-SDDC technique performs a segmentation process to detect the presence of pedestrians using a Simple Frame differencing-based background subtraction approach. Moreover, the distance among the detected pedestrians can be determined by employing Euclidean Distance. Furthermore, the support vector machine (SVM) model can be employed for the identification of SD. Finally, the CGO algorithm can be employed to adjust the parameters related to the SVM in such a way that the SD detection results can be enhanced. The performance analysis of the CGOML-SDDC methodology is tested by employing an SD dataset. An experimental values highlighted that the CGOML-SDDC technique reaches effective detection results over existing models.
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