Video Analytics on Social Distancing and Detecting Mask -A detailed Analysis

International Journal of Advanced Engineering Research and Science (IJAERS) ISSN: 2349-6495(P) | 2456-1908(O) Vol-8, Issue-5; May, 2021 Journal Home Page Available: https://ijaers.com/ Journal DOI: 10.22161/ijaers Article DOI: https://dx.doi.org/10.22161/ijaers.85.3

Video Analytics on Social Distancing and Detecting Mask A detailed Analysis S. Jayashri, M. Thoulath Kani, M.Vikram Pandi, B. Yazhisree, Dr. K. Lakshmi Department of Computer Science and Engineering, Periyar Maniammai Institute of Science and Technology, Thanjavur 613403, India

Received: 03 Feb 2021; Received in revised form: 30 Mar 2021; Accepted: 28 Apr 2021; Available online: 08 May 2021 ©2021 The Author(s). Published by AI Publication. This is an open access article under the CC BY license (https://creativecommons.org/licenses/by/4.0/).

Keywords— Deep distancing,Covid19, technology.

I.

Abstract— During this pandemic circumstance of Covid-19, social removing has become a standard general wellbeing mediation around the globe. Through social separating, wearing the face mask and try not to be in the group can slow the spread of Covid-19 illness. This survey is focused to inspect whether the people in a public maintains social distancing. It also checks whether every individual is wearing face mask.If both are not done, an alert is given to the public for maintain the social distance and it detect whether the individual is wearing mask or not.

Applying deep learning algorithm to maintain social distancing in public place through video analytics technology.

learning, Social video analytics

INTRODUCTION

Under the flow COVID-19 foundation, it is fundamentally imperative to control the spread of the infection. have shown that veil wearing can essentially diminish the danger of COVID-19 transmission.

Notwithstanding, it is absurd to expect that everybody is capable and able to wear a cover.

Video analytics It is an innovation that measures an advanced video signal utilizing an uncommon calculation to play out a security related capacity. for example, fixed calculation investigation that is intended to play out a particular assignment and search for a particular conduct. Video investigation is a vital segment of present-day metropolitan security, and when combined with computational examination, can have enormously extended usefulness including facial acknowledgment, movement recognition, traffic and group checking. This stands to identify the veil and social removing out in the open spots ,regardless of whether the individual wearing cover and keep up friendly separating or not .At present www.ijaers.com

restricted writing on exhibited compelling minimal effort frameworks for sending .In security and the executives areas ,there stay an extraordinary dependence on conventional manual checking of CCTV film using PC vision and ongoing mechanized investigation in substitution of difficult work lessens operational expenses as well as dispenses with human mistakes ,it tries to build up a biable arrangement prepared execution .numerous association today is anticipating adjust numerous fields have change their work way of life in computerized way thus ,continuous recognition frameworks are fundamental for such applications .we utilized different profound learning methods like yolov3 object identification.

Fig 1. Video analytics Page | 14

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II.

International Journal of Advanced Engineering Research and Science, 8(5)-2021 LITERATURE REVIEW

[1]. Lalitha r, Sagayasree.z, et.al. (2020). inspect whether every individual is wearing face mask in public places. If it is not, the drone sends alarm signal to nearby police station and also give alarm to the public. The proposed system uses an automated drone which is used to perform the inspection process.the drone is being constructed by considering the parameters such as components selection, payload calculation and then assembling the drone components and connecting the drone with the mission planner software for calibrating the drone for its stability. The trained yolov3 algorithm with the custom data set is being embedded in the drone’s camera. The algorithm can be embedded in public cameras and then details can be fetched to the camera unit same as the drone unit which receives details from the drone location details and store it in database.

[2]. Rucha visal, Atharva.T, et.al. (2020). emphasizes on a surveillance method which uses Open-CV, Computer vision and Deep learning to keep a track on the pedestrians and avoid overcrowding. implementation has been done using closed circuit television (CCTV) and Drones where the camera will detect the crowd with the help of object detection and compute the distance between them. The Euclidean distance between two people will be calculated in pixels and is compared with given standard distance and if it is observed to be less than the standard distance the local authorities or local police authorities will be notified.

[3]. George J Milne and Simon Xie (2020) evaluated a range of social distancing measures to determine which strategies are most effective in reducing the peak daily infection rate, and consequential pressure on the health care system. Simulation of virus transmission in this community model without interventions provided a baseline from which to compare alternative social distancing strategies. From this model-generated data, the rate of growth in cases, the magnitude of the epidemic peak, and the outbreak duration were obtained. The application of all four social distancing interventions: school closure, workplace non-attendance, increased case isolation, and community contact reduction is highly effective in flattening the epidemic curve, reducing the maximum daily case numbers, and lengthening outbreak durations. The most effective single intervention was found to be increasing case isolation, to 100% of children and 90% of adults. As strong social distancing intervention strategies had the most effect in reducing the epidemic peak, this strategy may be considered when weaker strategies are first tried and found to be less effective.

Trade-offs may need to be made between the effectiveness of social distancing strategies and population willingness to adhere to them. www.ijaers.com

[4]. Sanjay Kumar.S, Sonali Agarwal, et.al. (2020) proposes a deep learning-based framework for automating the task of monitoring social distancing using surveillance video. The proposed framework utilizes the YOLO v3 object detection model to segregate humans from the background and Deep sort approach to track the identified people with the help of bounding boxes and assigned IDs. The results of the YOLO v3 model are further compared with other popular state-of-the-art models, e.g., faster region-based CNN (convolution neural network) and single shot detector (SSD) in terms of mean average precision (mAP), frames per second (FPS) and loss values defined by object classification and localization. From this analysis, it is observed that the YOLO v3 with Deepsort tracking scheme displayed best results with balanced mAP and FPS score to monitor the social distancing in real-time.

[5]. Alessandro Vinciarelli, et.al. (2017) introduce the Visual Social Distancing (VSD) problem, defined as the automatic estimation of the inter-personal distance from an image, and the characterization of related people aggregations. VSD is pivotal for a non-invasive analysis to whether people comply with the SD restriction, and to provide statistics about the level of safety of specific areas whenever this constraint is violated. The aim is to truly detect potentially dangerous situations while avoiding false alarms (e.g., a family with children or relatives, an elder with their caregivers), all of this by complying with current privacy policies. then discuss how VSD relates with Social Signal Processing and indicate a path to research new Computer Vision methods that can possibly provide a solution to such problem. the future challenges related to the effectiveness of VSD systems, ethical implications and future application scenarios.

[6]. Simon Ching Man Yu, et.al. (2019) presented a low-cost and efficient approach that integrates the use of computational object recognition to perform fullyautomated identification, tracking, and counting of human traffic on camera video streams. Two software implementations are explored and the performance of these schemes is compared. Validation against controlled and non-controlled real-world environments is also demonstrated. The implementation provides automated video analytics for medium crowd density monitoring and tracking, eliminating labor-intensive tasks traditionally requiring human operation, with results indicating great reliability in real-life scenarios.

[7]. Dhananjay Kalbandeb, et.al (2020) propose a digital solution using Deep Learning technique which would alert them as soon as the violation of the social distancing is detected that is number of people more than the threshold (limit on the number of people allowed to be in a place, set by the government) or distance between two Page | 15

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people is less than the threshold distance. A video stream will be captured from the CCTV camera, with the help of Pose Net model we are detecting the humans and keeping a track of the number of humans present in the given live video stream, if the number of humans crosses the minimum threshold limit (set by the officials) or if the Euclidean distance between any two poses detected in the frame in less than say 3ft we alert the authorities incharged. This application will save time and quick analysis as in layman’s term the CCTV cameras will help simultaneously monitor each and every place of common gathering.

[8]. Li Wangand Dennis Sng (2015) Deep learning has recently achieved very promising results in a wide range of areas such as computer vision, speech recognition and natural language processing. Aims to learn hierarchical representations of data by using deep architecture models.

In a smart city, a lot of data (e.g., videos captured from many distributed sensors) need to be automatically processed and analyzed. In this paper, we review the deep learning algorithms applied to video analytics of smart city in terms of different research topics: object detection, object tracking, face recognition, image classification and scene labeling.

[9]. Gayatri Deore, Ramakrishna Bodhula, et.al. (2016) we propose a technique for masked face detection using four different steps of estimating distance from camera, eye line detection, facial part detection and eye detection.

The paper outlines the principles used in each of these steps and the use of commonly available algorithms of people detection and face detection. This unique approach for the problem has created a method simpler in complexity thereby making real time implementation feasible. Analysis of the algorithm’s performance on test video sequences gives useful insights to further improvements in the masked face detection performance.

[10]. Chengyi Qu, Songjie Wang, et.al (2019) propose a dynamic computation offloading and control framework, named DyCOCo, based on image impairment detection under various available network band width conditions.

DyCOCo framework demo features IoT devices in a test bed setup on the GENI infrastructure. results show that our DyCOCo approach can efficiently choose the suitable networking protocols and orchestrate both the camera control on the drone, and the computation offloading of the video analytics over limited edge computing/networking resources.

III.

OBJECTIVE

To examine whether individuals in a public spot keeps up friendly removing. It likewise checks whether each www.ijaers.com

individual is wearing face veil. The objective is to recognize occasions of semantic items that having a place with specific classes by applying profound learning method identifying human veil and actual distance is the necessities of this venture. It additionally checks every single distinctive individual. We assess scope of recognizing cover to figure out which methodologies are best in suffering in look every day by utilizing video Analytics. Social removing is characterized as keeping at least two meters (6 feet) aside from every person to dodge public contact. Further investigation additionally propose that social removing has significant monetary advantages.

Coronavirus may not be totally dispensed with temporarily, yet a mechanized framework that can help observing and examining social removing measures can extraordinarily profit our general public.

IV.

METHODOLGY

A. Software Implementation Our product bundle is executed on Python with the OpenSource Computer Vision (OpenCV) library. OpenCV upholds machine profound learning structures, and gives picture control, object ID, and movement following devices that are extraordinarily important for the advancement of programming in our unique situation.

B. Background Subtraction Foundation deduction is essentially identifying moving items in recordings utilizing static camera. the fundamental is to distinguishing the moving articles from the distinction between the current casing and a reference outline, which is classified "foundation picture" or "foundation model".

Foundation deduction is a strategy for isolating out forefront components from the foundation and is finished by creating a frontal area veil Background deduction method is significant for object following. In an external environment, flimsy environment, light changes, and reflections from surfaces on moving things would all have the option to decrease the limit of the reference layout allowance to separate establishment and closer view parts.

The foundation picture should be adequate to address the scene with no moving articles and be routinely refreshed so it adjusts to the changing luminance conditions and math settings. Helpless foundation picture may bring about helpless foundation deduction results, since it is to be deducted with the current picture to acquire the eventual outcome. Carried out three foundation deduction calculations going from fundamental system used to condition of craftsmanship procedures. Some basic methodologies plan to amplify speed and restricts the memory prerequisites which produce a low exact yield like the "outline contrast" technique and other modern Page | 16

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methodologies expects to accomplish the most noteworthy conceivable exactness under potential conditions.

the likelihood of the encased article being a specific class.

A large portion of these jumping boxes are killed in light of the fact that their certainty is low or in light of the fact that they are encasing a similar item as another bouncing box with high certainty score. This procedure is called non-greatest concealment.

V.

Fig 2. Background Subtraction

C.YOLO V3 It is the most recent variation of a famous item discovery calculation YOLO – You Only Look Once. YOLO works in the method of an item indicator as a blend of an finder and an recognizer. In PC vision draws near, a sliding window was utilized to search for objects at various areas and scales. Since this was a particularly costly activity, the angle proportion of the item was typically thought to be fixed. Early Deep Learning based item recognition calculations like the R-CNN and Fast R-CNN utilized a technique called specific to limit the quantity of bouncing boxes that the calculation needed to test. Another methodology brought Over accomplishment included checking the picture at numerous scales utilizing sliding windows-like systems done convolutionally. This was trailed by Faster R-CNN that utilized a Region Proposal Network (RPN) for distinguishing bouncing boxes that should have been tried. By cunning plan the highlights removed for perceiving objects, were likewise utilized by the RPN for proposing potential bouncing boxes hence saving a ton of calculation. YOLO then again moves toward the item location issue in a totally extraordinary manner. It advances the entire picture just a single time through the organization. SSD is another item discovery calculation that advances the picture once however a profound learning organization, yet YOLOv3 is a lot quicker than SSD while accomplishing truly equivalent precision. YOLOv3 gives quicker than Realtime results on a M40, Titanx or 1080 Ti GPUs. To start with, it isolates the picture into a 13×13 network of cells. The size of these 169 cells fluctuates relying upon the size of the info. For a 416×416 information size that we utilized in our analyses, the cell size was 32×32. Every cell is then answerable for anticipating various boxes in the picture. For each bouncing box, the organization additionally predicts the certainty that the jumping box really encases an item, and

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MAJOR RESULTS

We are focused on giving imaginative, strategic advances that ensure individuals and networks. Implementing social separating measures while amidst a progressing worldwide pandemic is an upward fight that each district and business is confronting today. It has been sent to get ready associations to adjust to the new standard to encourage appropriate adherence to rules and keep each local area part protected and sound.

A. Meaning of Project This task has pragmatic worth under the current setting of the COVID-19 pandemic. Pipeline is now fit for recognizing individuals with, without and inaccurately wearing covers with sensible exactness. For certain enhancements, we imagine that item can be utilized as a segment in a contact following framework. Item is likewise generally Computationally effective. The equipment limit for sending is low. This implies that item is less confined by financial plan or the degree of monetary improvement at the area of its organization and henceforth can arrive at more places where COVID-19 diseases present more danger to individuals.

B. Privacy Concerns Profound learning models have weaknesses. While it is feasible to lead antagonistic assaults on our model in the event that it is conveyed, such assaults are impossible not reason immediate, actual mischief to individuals whose countenances are distinguished. It merits referencing that, with least upgrades, our model is equipped for remembering identified countenances (e.g., through a face acknowledgment profound learning system). This is a probably use case if our model is fused into a contactfollowing framework where facial-acknowledgment and putting away faces are required. Facial highlights are by and large considered to have some degree of protection. In such cases, we should execute counter estimates, for example, carrying out safe profound learning models, jumbling put away faces and putting our item behind a safe solid highlight ensure the put away human countenances.

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VI.

International Journal of Advanced Engineering Research and Science, 8(5)-2021 IMPLEMENTATION

A. Dataset Veils assume a huge part in securing the soundness of people against infection spread in air, as is one of only a handful few safeguards accessible for COVID-19 without vaccination. Consequently, it is vital for us to identify whether an individual wear a cover and whether they wear accurately as a method for following the disease. As of now, information driven discovery and grouping models should be fitted with a dataset to work appropriately. Veil recognition and order dataset in this paper come from one of the most recent Face Mask Detection. This dataset is solid and steady for recognition and grouping models, that is, in each and every picture, there may be various focuses with various classes. This undertaking is the thing that Yolo structure intended for. Moreover, in light of this dataset, we additionally fabricated a less difficult dataset comprising of target cuts in the first pictures, to prepare and test Yolo-based characterization just models. In the preparation set, there are 3145 pictures, with 2546 with cover, 508 without veil, and 91 covers worn mistakenly.

The above numbers disclose to us that the dataset is restricted in size and is extremely one-sided towards the "Wearing Mask" class.

and tallness. (c) a gliding point certainty levels (d) a number demonstrating the anticipated class A variety of name names the video source is perused as an inerrable stream of casings of pictures. Each casing of picture is passed into our model at their unique tallness and width (e.g., 1080 pixels wide, 1920 pixels high). Our model produces derivation results adjusting to the above design.

We utilize the outcomes to draw the bouncing boxes, anticipating class names and certainty level for each recognized (face, face covers, face veils worn mistakenly) on this edge of picture. The drawn casing is then passed into a video encoder to be saved as a casing in the yield video. The outcome is another video with the above perceptions with MPEG-4 encoding.

The info video isn't altered in any capacity Processing recordings with OpenCV adds overhead to display expectation. The overhead comes from perusing outlines from the info video, drawing the perceptions and composing the attracted casing to the yield video. Model is very performant, accomplishing 2 edges for every second on a humble double center Intel Xeon CPU at 1920×1080 goal.

Fig 4. Block Diagram

VII.

Fig 3. Yolo

B. Video Processing We use OpenCV imagine the expectation brings about recordings. OpenCV upholds perusing surges of recordings from outside gadgets and documents from the nearby document framework. Given a prepared model on a veil discovery dataset, we anticipate that the output of the model should contain at any rate the accompanying fields: A variety of pictures utilized in the expectation and a variety of forecasts produced by the model, of tuples of the accompanying organization (a) x, y directions of the upper left corner of the jumping box, standardized to picture width and tallness. (b) x, y directions of the base right corner of the bouncing box, standardized to picture width www.ijaers.com

CONCLUSION

Real-time system to monitor the social distancing and using the proposed critical social density to avoid overcrowding. We are focused on giving imaginative, strategic advances that ensure individuals and networks.

Implementing social separating measures while amidst a progressing worldwide pandemic is an upward fight that each district and business is confronting today. It has been sent to get ready associations to adjust to the new standard to encourage appropriate adherence to rules and keep each local area part protected and sound. This task has pragmatic worth under the current setting of the COVID19 pandemic. Pipeline is now fit for recognizing individuals with, without and inaccurately wearing covers with sensible exactness. For certain enhancements, we imagine that item can be utilized as a segment in a contact following framework. Item is likewise generally Computationally effective. The equipment limit for sending is low. This implies that item is less confined by Page | 18

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financial plan or the degree of monetary improvement at the area of its organization and henceforth can arrive at more places where COVID- 19 diseases present more danger to individuals.

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