scholarly journals Artificial Intelligence and Deep Learning for Weapon Identification in Security Systems

2021 ◽  
Vol 2089 (1) ◽  
pp. 012079
Author(s):  
Makkena Brahmaiah ◽  
Srinivasa Rao Madala ◽  
Ch Mastan Chowdary
Keyword(s):  
Artificial Intelligence ◽  
Computer Vision ◽  
Deep Learning ◽  
Video Surveillance ◽  
Real World ◽  
Crime Rates ◽  
Surveillance Systems ◽  
Security Systems ◽  
Trade Off ◽  
Wide Range

Abstract As crime rates rise at large events and possibly lonely places, security is always a top concern in every field. A wide range of issues may be solved with the use of computer vision, including anomalous detection and monitoring. Intelligence monitoring is becoming more dependent on video surveillance systems that can recognise and analyse scene and anomaly occurrences. Using SSD and Faster RCNN techniques, this paper provides automated gun (or weapon) identification. Use of two different kinds of datasets is included in the proposed approach. As opposed to the first dataset, the second one comprises pictures that have been manually tagged. However, the trade-off between speed and precision in real-world situations determines whether or not each method will be useful.

scholarly journals Deep learning-enabled medical computer vision

2021 ◽  
Vol 4 (1) ◽  
Author(s):  
Andre Esteva ◽  
Katherine Chou ◽  
Serena Yeung ◽  
Nikhil Naik ◽  
Ali Madani ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Neural Networks ◽  
Computer Vision ◽  
Deep Learning ◽  
Medical Imaging ◽  
Real World ◽  
Recent Progress ◽  
Medical Applications ◽  
Modern Computer ◽  
Medical Computer

AbstractA decade of unprecedented progress in artificial intelligence (AI) has demonstrated the potential for many fields—including medicine—to benefit from the insights that AI techniques can extract from data. Here we survey recent progress in the development of modern computer vision techniques—powered by deep learning—for medical applications, focusing on medical imaging, medical video, and clinical deployment. We start by briefly summarizing a decade of progress in convolutional neural networks, including the vision tasks they enable, in the context of healthcare. Next, we discuss several example medical imaging applications that stand to benefit—including cardiology, pathology, dermatology, ophthalmology–and propose new avenues for continued work. We then expand into general medical video, highlighting ways in which clinical workflows can integrate computer vision to enhance care. Finally, we discuss the challenges and hurdles required for real-world clinical deployment of these technologies.

scholarly journals A Survey on Bias and Fairness in Machine Learning

2021 ◽  
Vol 54 (6) ◽  
pp. 1-35
Author(s):  
Ninareh Mehrabi ◽  
Fred Morstatter ◽  
Nripsuta Saxena ◽  
Kristina Lerman ◽  
Aram Galstyan
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Deep Learning ◽  
Real World ◽  
State Of The Art ◽  
Future Directions ◽  
Discriminatory Behavior ◽  
Real World Applications ◽  
Near Future ◽  
Different Sources

With the widespread use of artificial intelligence (AI) systems and applications in our everyday lives, accounting for fairness has gained significant importance in designing and engineering of such systems. AI systems can be used in many sensitive environments to make important and life-changing decisions; thus, it is crucial to ensure that these decisions do not reflect discriminatory behavior toward certain groups or populations. More recently some work has been developed in traditional machine learning and deep learning that address such challenges in different subdomains. With the commercialization of these systems, researchers are becoming more aware of the biases that these applications can contain and are attempting to address them. In this survey, we investigated different real-world applications that have shown biases in various ways, and we listed different sources of biases that can affect AI applications. We then created a taxonomy for fairness definitions that machine learning researchers have defined to avoid the existing bias in AI systems. In addition to that, we examined different domains and subdomains in AI showing what researchers have observed with regard to unfair outcomes in the state-of-the-art methods and ways they have tried to address them. There are still many future directions and solutions that can be taken to mitigate the problem of bias in AI systems. We are hoping that this survey will motivate researchers to tackle these issues in the near future by observing existing work in their respective fields.

Security Applications Using Computer Vision

2013 ◽  
pp. 1093-1110
Author(s):  
Sreela Sasi
Keyword(s):  
Computer Vision ◽  
Power Plant ◽  
Video Surveillance ◽  
Homeland Security ◽  
Port Security ◽  
Video Cameras ◽  
Security Applications ◽  
Wide Range ◽  
Cargo Inspection ◽  
Computer Vision Systems

Computer vision plays a significant role in a wide range of homeland security applications. The homeland security applications include: port security (cargo inspection), facility security (embassy, power plant, bank), and surveillance (military or civilian), et cetera. Video surveillance cameras are placed in offices, hospitals, banks, ports, parking lots, parks, stadiums, malls, train stations, airports, et cetera. The challenge is not for acquiring surveillance data from these video cameras, but for identifying what is valuable, what can be ignored, and what demands immediate attention. Computer vision systems attempt to construct meaningful and explicit descriptions of the environment or scene captured in an image. A few Computer Vision based security applications are presented here for securing building facility, railroad (Objects on railroad, and red signal detection), and roads.

Human-Compatible Artificial Intelligence

2021 ◽  
pp. 3-23
Author(s):  
Stuart Russell
Keyword(s):  
Artificial Intelligence ◽  
Decision Making ◽  
Standard Model ◽  
Real World ◽  
Technical Aspects ◽  
Alan Turing ◽  
Wide Range ◽  
The Standard Model ◽  
Formal Class

Following the analysis given by Alan Turing in 1951, one must expect that AI capabilities will eventually exceed those of humans across a wide range of real-world-decision making scenarios. Should this be a cause for concern, as Turing, Hawking, and others have suggested? And, if so, what can we do about it? While some in the mainstream AI community dismiss the issue, I will argue that the problem is real: we have to work out how to design AI systems that are far more powerful than ourselves while ensuring that they never have power over us. I believe the technical aspects of this problem are solvable. Whereas the standard model of AI proposes to build machines that optimize known, exogenously specified objectives, a preferable approach would be to build machines that are of provable benefit to humans. I introduce assistance games as a formal class of problems whose solution, under certain assumptions, has the desired property.

Sixth Sense Technology

2016 ◽  
pp. 188-215 ◽  
Author(s):  
Zeenat S. AlKassim ◽  
Nader Mohamed
Keyword(s):  
Artificial Intelligence ◽  
Image Processing ◽  
Computer Vision ◽  
Augmented Reality ◽  
Human Computer Interaction ◽  
Gesture Recognition ◽  
Real World ◽  
Digital World ◽  
The Future ◽  
Computer Interaction

In this chapter, the authors discuss a unique technology known as the Sixth Sense Technology, highlighting the future opportunities of such technology in integrating the digital world with the real world. Challenges in implementing such technologies are also discussed along with a review of the different possible implementation approaches. This review is performed by exploring the different inventions in areas similar to the Sixth Sense Technology, namely augmented reality (AR), computer vision, image processing, gesture recognition, and artificial intelligence and then categorizing and comparing between them. Lastly, recommendations are discussed for improving such a unique technology that has the potential to create a new trend in human-computer interaction (HCI) in the coming years.

scholarly journals A deep learning platooning-based video information-sharing Internet of Things framework for autonomous driving systems

2019 ◽  
Vol 15 (11) ◽  
pp. 155014771988313 ◽  
Author(s):  
Zishuo Zhou ◽  
Zahid Akhtar ◽  
Ka Lok Man ◽  
Kamran Siddique
Keyword(s):  
Artificial Intelligence ◽  
Computer Vision ◽  
Deep Learning ◽  
Internet Of Things ◽  
Information Sharing ◽  
Communication Technology ◽  
Empirical Evaluation ◽  
Traffic Lights ◽  
Video Information ◽  
Safety And Stability

To enhance the safety and stability of autonomous vehicles, we present a deep learning platooning-based video information-sharing Internet of Things framework in this study. The proposed Internet of Things framework incorporates concepts and mechanisms from several domains of computer science, such as computer vision, artificial intelligence, sensor technology, and communication technology. The information captured by camera, such as road edges, traffic lights, and zebra lines, is highlighted using computer vision. The semantics of highlighted information is recognized by artificial intelligence. Sensors provide information on the direction and distance of obstacles, as well as their speed and moving direction. The communication technology is applied to share the information among the vehicles. Since vehicles have high probability to encounter accidents in congested locations, the proposed system enables vehicles to perform self-positioning with other vehicles in a certain range to reinforce their safety and stability. The empirical evaluation shows the viability and efficacy of the proposed system in such situations. Moreover, the collision time is decreased considerably compared with that when using traditional systems.

scholarly journals Deep Learning- Based Surveillance System using Face Recognition

2020 ◽  
Vol 32 ◽  
pp. 03011
Author(s):  
Divya Kapil ◽  
Aishwarya Kamtam ◽  
Akhil Kedare ◽  
Smita Bharne
Keyword(s):  
Neural Network ◽  
Image Processing ◽  
Deep Learning ◽  
Face Recognition ◽  
Convolutional Neural Network ◽  
Surveillance System ◽  
Surveillance Systems ◽  
Learning Method ◽  
Security Systems ◽  
The Face

Surveillance systems are used for the monitoring the activities directly or indirectly. Most of the surveillance system uses the face recognition techniques to monitor the activities. This system builds the automated contemporary biometric surveillance system based on deep learning. The application of the system can be used in various ways. The face prints of the persons will be stored inside the database with relevant statistics and does the face recognition. When any unknown face is recognized then alarm will ring so one can alert the security systems and in addition actions will be taken. The system learns changes while detecting faces automatically using deep learning and gain correct accuracy in face recognition. A deep learning method including Convolutional Neural Network (CNN) is having great significance in the area of image processing. This system can be applicable to monitor the activities for the housing society premises.

scholarly journals A Face Image Virtualization Mechanism for Privacy Intrusion Prevention in Healthcare Video Surveillance Systems

Symmetry ◽  
2020 ◽  
Vol 12 (6) ◽  
pp. 891 ◽  
Author(s):  
Jinsu Kim ◽  
Namje Park
Keyword(s):  
Video Surveillance ◽  
Visual Information ◽  
Face Image ◽  
Video Data ◽  
Surveillance Systems ◽  
Image Information ◽  
Video Information ◽  
Face Area ◽  
Blockchain Technology ◽  
Wide Range

Closed-circuit television (CCTV) and video surveillance systems (VSSs) are becoming increasingly more common each year to help prevent incidents/accidents and ensure the security of public places and facilities. The increased presence of VSS is also increasing the number of per capita exposures to CCTV cameras. To help protect the privacy of the exposed objects, attention is being drawn to technologies that utilize intelligent video surveillance systems (IVSSs). IVSSs execute a wide range of surveillance duties—from simple identification of objects in the recorded video data, to understanding and identifying the behavioral patterns of objects and the situations at the incident/accident scenes, as well as the processing of video information to protect the privacy of the recorded objects against leakage. Besides, the recorded privacy information is encrypted and recorded using blockchain technology to prevent forgery of the image. The technology herein proposed (the “proposed mechanism”) is implemented to a VSS, where the mechanism converts the original visual information recorded on a VSS into a similarly constructed image information, so that the original information can be protected against leakage. The face area extracted from the image information is recorded in a separate database, allowing the creation of a restored image that is in perfect symmetry with the original image for images with virtualized face areas. Specifically, the main section of this study proposes an image modification mechanism that inserts a virtual face image that closely matches a predetermined similarity and uses a blockchain as the storage area.

Artificial intelligence based on deep learning for differential diagnosis between benign and malignant pulmonary nodules: A real-world, multicenter, diagnostic study.

2020 ◽  
Vol 38 (15_suppl) ◽  
pp. 9037-9037
Author(s):  
Tao Xu ◽  
Chuoji Huang ◽  
Yaoqi Liu ◽  
Jing Gao ◽  
Huan Chang ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Lung Cancer ◽  
Deep Learning ◽  
Diagnostic Accuracy ◽  
Real World ◽  
Pulmonary Nodules ◽  
Ct Images ◽  
Test Method ◽  
Histopathological Diagnosis ◽  
Diagnostic Study

9037 Background: Lung cancer is the most common cancer worldwide. Artificial intelligence (AI) platform using deep learning algorithms have made a remarkable progress in improving diagnostic accuracy of lung cancer. But AI diagnostic performance in identifying benign and malignant pulmonary nodules still needs improvement. We aimed to validate a Pulmonary Nodules Artificial Intelligence Diagnostic System (PNAIDS) by analyzing computed tomography (CT) imaging data. Methods: This real-world, multicentre, diagnostic study was done in five different tier hospitals in China. The CT images of patients, who were aged over 18 years and never had previous anti-cancer treatments, were retrieved from participating hospitals. 534 eligible patients with 5-30mm diameter pulmonary nodules identified by CT were planning to confirm with histopathological diagnosis. The performance of PNAIDS was also compared with respiratory specialists and radiologists with expert or competent degrees of expertise as well as Mayo Clinic’s model by area under the curve (AUC) and evaluated differences by calculating the 95% CIs using the Z-test method. 11 selected participants were tested circulating genetically abnormal cells (CACs) before surgery with doctors suggested. Results: 611 lung CT images from 534 individuals were used to test PNAIDS. The diagnostic accuracy, valued by AUC, in identifying benign and malignant pulmonary nodules was 0.765 (95%CI [0.729 - 0.798]). The diagnostic sensitivity of PNAIDS is 0.630(0.579 – 0.679), specificity is 0.753 (0.693 – 0.807). PNAIDS achieved diagnostic accuracy similar to that of the expert respiratory specialists (AUC difference: 0.0036 [-0.0426 - 0.0497]; p = 0.8801) and superior when compared with Mayo Clinic’s model (0.120 [0.0649 - 0.176], p < 0·0001), expert radiologists (0.0620 [0.0124 - 0.112], p = 0.0142) and competent radiologists (0.0751 [0.0248 - 0.125], p = 0.0034). 11 selected participants were suggested negative in AI results but positive in respiratory specialists’ result. 8 of them were malignant in histopathological diagnosis with tested more than 3 CACs in their blood. Conclusions: PNAIDS achieved high diagnostic accuracy in differential diagnoses between benign and malignant pulmonary nodules, with diagnostic accuracy similar to that of expert respiratory specialists and was superior to that of Mayo Clinic’s model and radiologists. CACs may be able to assist CT-based AI in improving their effectiveness but it still need more data to be proved. Clinical trial information: ChiCTR1900026233.

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