Global Journal of Computer Science and Technology, D: Neural & Artificial Intelligence, Volume 22 Issue 1

.To accomplish this, we use a computation algorithm that we created ourselves. As with other systems, if someone behave incorrectly, it sends a notification to the location chief. When analyzing the risk of social distance to get a better accuracy this paper used the SSD-mobilnet model. In each frame need to be more accurate. Therefore to get better detection for bad visibility areas used CLAHE prepossessing method to identify objects [11]. Most of the researchers only considered about detect social distance [13]. This paper is most related to analyze the risk of percentage. Based on these percentages, can easily detect whether the area is bad or good. III. M ethodology Fig. 1: Overall System diagram According to the system overview diagram Fig.1, initially system gets CCTV footage as a input and same CCTV footage goes through four sub risk analysis functions separately and estimate the risk status. For the estimating the overall risk percentage, divide the 100% equally among four sub functions and each function gets 25%. if one function is totally violating, then added each sub function 25% percentage to the total overall risk. finally if the total risk percentage is greater than 75% email will be send, informing that area will be a risky place. a) Density Risk Analysis Fig. 2: An image of a density risk system overview The proposed system analysis the density risk in a particular area at a particular time. According to Fig.2 the system gets video frames as input and then in the first video frame user must select the area where they want to measure the density risk using four mouse click points. Then this area is a Polygon shape rectangle area. Then according to the width and length of the area, the system estimates the maximum people count allowed in that particular area according to Fig.3 using predefined formula. After that, the system takes the real- time people/visitors count inside the area in each video frame. finally, the system comparing both the Maximum people count and real-time people count inside the area, and if the real-time people count higher than the Maximum people count that area is a High-Risk area, email will be send, informing that area will be a risky place. Object detection and tracking are one of the main parts of this function.Yolov3 is used to detect an object in the frame. Yolov3 is a unique neural network that predicts bounding boxes and class probabilities directly from complete images in a single evaluation. The Yolov3 configure (cfg file) and weight file trained on the detect 80 classes objects. However, People/visitors type object detection is only needed for the density risk estimation. So then did some transfer learning (hyper- parameter changing – max batches, filters, classes) for the Yolov3.cfg file (model architecture file) and re-trained using Google’s Open Images, then generated the new weight file and it used for the people/visitors object detection in the video frames and also shapely python libraries and Open CV techniques used for the estimate the length and width of the area in the video frame. Fig. 3: Example for maximum people estimation b) Social Distance Risk Analysis Proposed method was developed to detect the safety distance between people in public areas. The CNN based methods such as YOLO (You look only once), SSD (Single shot Detector) computer vision and machine learning techniques are employed in this project. SSD Object Detection extracts feature map using a base deep learning network, which are CNN based classifiers, and applies convolution filters to finally detect objects. Here are the steps. Mainly the open source open-cv is used to divide video into small frames. The SSD-MobileNet-caffe model which is used to detect the objects and analyse the bounding boxes. Global Journal of Computer Science and Technology Volume XXII Issue I Version I 11 ( )D © 2022 Global Journals Human Tracking and Profiling for Risk Management Year 2022