Human detection techniques for real time surveillance: a comprehensive survey

Ansari, M. A.; Singh, Dushyant Kumar

doi:10.1007/s11042-020-10103-4

Cited by 33 publications

(16 citation statements)

References 142 publications

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“…Additionally, the face mask trained model was acquired during the learning phase and deployed at the testing or run time phase. A Face Detection [1,23] model is essential to observe faces. Finally, with the help of the obtained model, the system will predict 'wearing mask' or 'no masked' faces by simply drawing a box around the border.…”

Section: Proposed Methodologymentioning

confidence: 99%

AI-based face mask detection system: a straightforward proposition to fight with Covid-19 situation

Jayaswal¹,

Dixit²

2022

Multimed Tools Appl

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The whole world is suffering from a novel coronavirus, which has become an epidemic. According to a World Health Organization report, this is a communicable disease, i.e., it transfers from an infected person to a healthy person. Therefore, wearing a mask is the most important precaution to protect from COVID-19. This paper presented a deep learning-based approach to design a Face Mask Detection framework to predict whether a person is wearing a mask or not. The proposed method uses a Single Shot Multibox detector as a face detector model and a deep Inception V3 architecture (SSDIV3) to extract the pertinent features of images and discriminate them in mask and without masks labels. Optimizing the SSDIV3 approach using different modeling parameters is a genuine contribution of this work. In addition to this, the system is tested and analyzed on VGG16, VGG19, Xception, Mobilenet V2 models at different modeling parameters. Furthermore, two synthesized novel Face Mask Datasets are introduced containing diversified masks (2d_printed, 3d_printed, handkerchief, transparent, natural-looking mask appearance masks) and unmask images of humans collected in outdoor and indoor environments such as parks, homes, laboratories. The experiment outcomes demonstrate that the proposed system has achieved an accuracy of 98% on the synthesized benchmark datasets, which comparatively outperforms other state-of-art methods and datasets in a real-time environment.

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Section: Proposed Methodologymentioning

confidence: 99%

AI-based face mask detection system: a straightforward proposition to fight with Covid-19 situation

Jayaswal¹,

Dixit²

2022

Multimed Tools Appl

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“…However, the only challenge for single-stage detectors is detection accuracy. The Mask R-CNN can detect better in the case of object detection [17]. Thus, to better detect the pedestrians for the pedestrian attribute recognition task, we introduced Mask R-CNN in our proposed framework.…”

Section: Related Workmentioning

confidence: 99%

A Framework for Pedestrian Attribute Recognition Using Deep Learning

et al. 2022

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The pedestrian attribute recognition task is becoming more popular daily because of its significant role in surveillance scenarios. As the technological advances are significantly more than before, deep learning came to the surface of computer vision. Previous works applied deep learning in different ways to recognize pedestrian attributes. The results are satisfactory, but still, there is some scope for improvement. The transfer learning technique is becoming more popular for its extraordinary performance in reducing computation cost and scarcity of data in any task. This paper proposes a framework that can work in surveillance scenarios to recognize pedestrian attributes. The mask R-CNN object detector extracts the pedestrians. Additionally, we applied transfer learning techniques on different CNN architectures, i.e., Inception ResNet v2, Xception, ResNet 101 v2, ResNet 152 v2. The main contribution of this paper is fine-tuning the ResNet 152 v2 architecture, which is performed by freezing layers, last 4, 8, 12, 14, 20, none, and all. Moreover, data balancing techniques are applied, i.e., oversampling, to resolve the class imbalance problem of the dataset and analysis of the usefulness of this technique is discussed in this paper. Our proposed framework outperforms state-of-the-art methods, and it provides 93.41% mA and 89.24% mA on the RAP v2 and PARSE100K datasets, respectively.

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“…Ansari et.al. classified pedestrian detection techniques into four main approaches as face feature based, motion feature based, body appearance and deep learning based methods [6]. Deep learning based methods including CNN, Fast R-CNN, Single Shot MultiBox Detector (SSD) and YOLO distinguished from others in terms of achieved high success rates and reliability [2].…”

Section: Related Workmentioning

confidence: 99%

Artificial Intelligence Based Social Distance Monitoring in Public Areas

Albayrak¹

2022

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COVID-19 is an infectious disease caused by a newly discovered coronavirus called SARS-CoV-2. There are two ways of contamination risk, namely spreading through droplets or aerosol-type spreading into the air with people's speech in crowded environments. The best way to prevent the spread of COVID-19 in a crowd public area is to follow social distance rules. Violation of the social distance is a common situation in areas where people frequently visit such as hospitals, schools and shopping centers. In this study, an artificial intelligence-based social distance determination study was developed in order to detect social distance violations in crowded areas. Within the scope of the study, a new dataset was proposed to determine social distance between pedestrians. The YOLOv3 algorithm, which is very successful in object detection, was compared with the SSD-MobileNET, which is considered to be a light weighted model, and the traditionally handcrafted methods Haar-like cascade and HOG methods. Inability to obtain depth information, which is one of the biggest problems encountered in monocular cameras, has been tried to be eliminated by perspective transformation. In this way, the social distance violation detected in specific area is notified by the system to the relevant people with a warning.

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Human detection techniques for real time surveillance: a comprehensive survey

Cited by 33 publications

References 142 publications

AI-based face mask detection system: a straightforward proposition to fight with Covid-19 situation

AI-based face mask detection system: a straightforward proposition to fight with Covid-19 situation

A Framework for Pedestrian Attribute Recognition Using Deep Learning

Artificial Intelligence Based Social Distance Monitoring in Public Areas

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