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Статті в журналах з теми "Wearable video devices in police work"
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Apostolakis, Konstantinos C., Nikolaos Dimitriou, George Margetis, Stavroula Ntoa, Dimitrios Tzovaras, and Constantine Stephanidis. "DARLENE – Improving situational awareness of European law enforcement agents through a combination of augmented reality and artificial intelligence solutions." Open Research Europe 1 (January 21, 2022): 87. http://dx.doi.org/10.12688/openreseurope.13715.2.
Повний текст джерелаАнотація:
Background: Augmented reality (AR) and artificial intelligence (AI) are highly disruptive technologies that have revolutionised practices in a wide range of domains, including the security sector. Several law enforcement agencies (LEAs) employ AI in their daily operations for forensics and surveillance. AR is also gaining traction in security, particularly with the advent of affordable wearable devices. Equipping police officers with the tools to facilitate an elevated situational awareness (SA) in patrolling and tactical scenarios is expected to improve LEAs’ safety and capacity to deliver crucial blows against terrorist and/or criminal threats. Methods: In this paper we present DARLENE, an ecosystem incorporating novel AI techniques for activity recognition and pose estimation tasks, combined with a wearable AR framework for visualization of the inferenced results via dynamic content adaptation according to the wearer’s stress level and operational context. The concept has been validated with end-users through co-creation workshops, while the decision-making mechanism for enhancing LEAs’ SA has been assessed with experts. Regarding computer vision components, preliminary tests of the instance segmentation method for humans’ and objects’ detection have been conducted on a subset of videos from the RWF-2000 dataset for violence detection, which have also been used to test a human pose estimation method that has so far exhibited impressive results, constituting the basis of further developments in DARLENE. Results: Evaluation results highlight that target users are positive towards the adoption of the proposed solution in field operations, and that the SA decision-making mechanism produces highly acceptable outcomes. Evaluation of the computer vision components yielded promising results and identified opportunities for improvement. Conclusions: This work provides the context of the DARLENE ecosystem and presents the DARLENE architecture, analyses its individual technologies, and demonstrates preliminary results, which are positive both in terms of technological achievements and user acceptance of the proposed solution.
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Brucato, Ben. "Policing Made Visible: Mobile Technologies and the Importance of Point of View." Surveillance & Society 13, no. 3/4 (October 26, 2015): 455–73. http://dx.doi.org/10.24908/ss.v13i3/4.5421.
Повний текст джерелаАнотація:
Cameras are ubiquitous and increasingly mobile. While CCTV has captured considerable attention by surveillance researchers, the new visibility of police activities is increasingly produced by incidental sousveillance and wearable on-officer camera systems. This article considers advocacy for policing’s new visibility, contrasting that of police accountability activists who film police with designers and early adopters of on-officer cameras. In both accounts, these devices promise accountability by virtue of their mechanical objectivity. However, to each party, accountability functions rather differently. By attending to the social and legal privileging of police officers’ perspectives, the article provides an explanation for design decisions that produced Taser’s AXON Flex on-officer cameras and for why police are embracing these new technologies. Critics of these cameras cite privacy concerns, officer discretion in operating cameras, and department disclosure of footage. Nonetheless, advocates of police accountability often presume more video documenting police use of force is always helpful. However, the utility of surveillance video is conditioned by point of view. Police agencies in the U.S. are rapidly adopting on-officer camera systems, because they acknowledge ubiquitous surveillance and that these devices aid in nullifying third-party documentation in favor of a perspective that favors officers. As such, these cameras are counter-sousveillance technologies.
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Suminski, Richard R., Gregory M. Dominick, and Norman J. Wagner. "A Direct Observation Video Method for Describing COVID-19 Transmission Factors on a Micro-Geographical Scale: Viral Transmission (VT)-Scan." International Journal of Environmental Research and Public Health 18, no. 17 (September 3, 2021): 9329. http://dx.doi.org/10.3390/ijerph18179329.
Повний текст джерелаАнотація:
The COVID-19 pandemic severely affected many aspects of human life. While most health agencies agree mask wearing and physical distancing reduce viral transmission, efforts to improve the assessment of these behaviors are lacking. This study aimed to develop a direct observation video method [Viral Transmission (VT)-Scan] for assessing COVID-19 transmission behaviors and related factors (e.g., environmental setting). A wearable video device (WVD) was used to obtain videos of outdoor, public areas. The videos were examined to extract relevant information. All outcomes displayed good to excellent intra- and inter-reliability with intra-class correlation coefficients ranging from 0.836 to 0.997. The majority of people had a mask (60.8%) but 22.1% of them wore it improperly, 45.4% were not physical distancing, and 27.6% were simultaneously mask and physical distancing non-compliant. Transmission behaviors varied by demographics with white, obese males least likely to be mask-compliant and white, obese females least likely to physical distance. Certain environments (e.g., crosswalks) were identified as “hot spots” where higher rates of adverse transmission behaviors occurred. This study introduces a reliable method for obtaining objective data on COVID-19 transmission behaviors and related factors which may be useful for agent-based modeling and policy formation.
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Suma, TP, and G. Rekha. "STUDY ON IOT BASED WOMEN SAFETY DEVICES WITH SCREAMING DETECTION AND VIDEO CAPTURING." International Journal of Engineering Applied Sciences and Technology 6, no. 7 (November 1, 2021): 257–62. http://dx.doi.org/10.33564/ijeast.2021.v06i07.041.
Повний текст джерелаАнотація:
To propose an idea for implementing wearable IoT devices using the Raspberry Pi, which is equipped with a sound sensor, camera module, GPS, and GSM. When the sound sensor detects screaming, the SVM (support vector machine) algorithm in machine learning eliminates unnecessary noise and evaluates only the victim screaming. After assessing the victim's scream, the gadget promptly activates the camera module, which takes 30 seconds of footage. GPS will track the geographical coordinates and, using the GSM module, will send an alert message and an emergency call to the nearest police station. When the victim activates the switch in the second scenario, the camera module is immediately activated, and a 30-second clip is captured. The GPS will be active, and GSM will send an alert message with the status. The idea's major goal is to present a smart gadget for a woman that is entirely comfy and portable. When compared to other existing safety devices, the first and most crucial difference is the smart band's ability to shrink the size of gadgets.
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Ma, Xiaochun. "Teaching Mode of Augmented Reality College English Listening and Speaking Supported by Wearable Technology." Wireless Communications and Mobile Computing 2022 (May 9, 2022): 1–10. http://dx.doi.org/10.1155/2022/2181512.
Повний текст джерелаАнотація:
With the development of modern technology, wearable technology has become more and more popular in the actual college English listening and speaking teaching mode. Wearable devices, as the name suggests, are smart electronic products that can be worn on the human body, such as smart glasses, smart watches, smart rings and smart clothing. Smart watches and ordinary electronic products are very different from wearable devices. Wearable devices give us a more intelligent and technological experience. Wearable technology has brought new changes and challenges to the realistic college English listening and speaking teaching mode. Wearable technology has become a hot topic and frontier in the field of science and technology. It has been widely used in education, medical, and industrial fields. It provides unprecedented convenience for people to learn, work, and live. Augmented reality technology is a kind of technology that skillfully integrates virtual information with the real world. After the simulation of computer-generated text, images, 3D models, music, video, and other virtual information, it is applied to the real world, and the two kinds of information complement each other, thus realizing the “enhancement” of the real world. This paper gives a brief overview of the definition of wearable technology, the classification, and main features of wearable technology and combines it with college English listening and speaking teaching mode to improve the classroom learning effect. Through the questionnaire survey, we know that wearable technology is a teacher’s teaching, and students’ learning provides great convenience. Wearable technology greatly enhances the learning and listening fun of students.
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Shree, Vidya, and Sharanabasappa. "WEARABLE REAL TIME HEALTH TRACKERS AND ASSISTED-MOBILITY FOR NON-AMBULANT PATIENTS." International Journal of Research -GRANTHAALAYAH 5, no. 4RACSIT (April 30, 2017): 41–46. http://dx.doi.org/10.29121/granthaalayah.v5.i4racsit.2017.3349.
Повний текст джерелаАнотація:
This paper presents an approach to facilitate mobility for the non-ambulant patients using eye movements and wearable health tracking. The patient’s wheelchair movement is controlled using eye ball movements and also some biomedical assistance functionalities are considered to match the current day needs. The proposed work first detects face from input video, then eye portion will be localized, and finally eye ball (pupil) is detected and tracked using computer vision techniques. The direction of movement is assessed and a command is disseminated to the wheelchair control system. The wearable sensors and\or alarms mounted on patients will update current health status indications on to the monitoring panels. The wearable devices have evolved very smartly which are capable enough to take care of the patient health in real time even during assisted mobility.
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Perez, Alfredo J., Sherali Zeadally, Scott Griffith, Luis Y. Matos Garcia, and Jaouad A. Mouloud. "A User Study of a Wearable System to Enhance Bystanders’ Facial Privacy." IoT 1, no. 2 (October 10, 2020): 198–217. http://dx.doi.org/10.3390/iot1020013.
Повний текст джерелаАнотація:
The privacy of users and information are becoming increasingly important with the growth and pervasive use of mobile devices such as wearables, mobile phones, drones, and Internet of Things (IoT) devices. Today many of these mobile devices are equipped with cameras which enable users to take pictures and record videos anytime they need to do so. In many such cases, bystanders’ privacy is not a concern, and as a result, audio and video of bystanders are often captured without their consent. We present results from a user study in which 21 participants were asked to use a wearable system called FacePET developed to enhance bystanders’ facial privacy by providing a way for bystanders to protect their own privacy rather than relying on external systems for protection. While past works in the literature focused on privacy perceptions of bystanders when photographed in public/shared spaces, there has not been research with a focus on user perceptions of bystander-based wearable devices to enhance privacy. Thus, in this work, we focus on user perceptions of the FacePET device and/or similar wearables to enhance bystanders’ facial privacy. In our study, we found that 16 participants would use FacePET or similar devices to enhance their facial privacy, and 17 participants agreed that if smart glasses had features to conceal users’ identities, it would allow them to become more popular.
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Zhadan, D. O., M. V. Mordvyntsev, and D. V. Pashniev. "Tracking illegal activities using video surveillance systems: a review of the current state of research." Law and Safety 92, no. 1 (March 29, 2024): 78–89. http://dx.doi.org/10.32631/pb.2024.1.07.
Повний текст джерелаАнотація:
The current state of research on the use of the neural networks under martial law to identify offenders committing illegal acts, prevent acts of terrorism, combat sabotage groups in cities, track weapons and control traffic is considered. The methods of detecting illegal actions, weapons, face recognition and traffic violations using video surveillance cameras are analysed. It is proposed to introduce the studied methods into the work of “smart” video surveillance systems in Ukrainian settlements.
The most effective means of reducing the number of offences is the inevitability of legal liability for offences, so many efforts in law enforcement are aimed at preventing offences. Along with public order policing by patrol police, video surveillance is an effective way to prevent illegal activities in society. Increasing the coverage area of cameras and their number helps to ensure public safety in the area where they are used. However, an increase in the number of cameras creates another problem which is the large amount of video data that needs to be processed. To solve the problem of video data processing, various methods are used, the most modern of which is the use of artificial intelligence to filter a large amount of data from video cameras and the application of various video processing algorithms. The ability to simultaneously process video data from many CCTV cameras without human intervention not only contributes to public safety, but also improves the work of patrol police. The introduction of smart video surveillance systems allows monitoring the situation in public places around the clock, even if there is no police presence in the area.
In the reviewed studies of video surveillance systems, neural networks, in particular MobileNet V2, YOLO, mYOLOv4-tiny, are used to track illegal actions, criminals and weapons, which are trained on large amounts of video and photo data. It has been found that although neural networks used to require a lot of computing power, they can now be used in IoT systems and smartphones, and this contributes to the fact that more video surveillance devices can be used to monitor the situation.
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Bai, Ziyu. "WIFI-Based Human Identification of gait recognition in muti-scenario." International Journal of Computer Science and Information Technology 1, no. 1 (December 30, 2023): 1–9. http://dx.doi.org/10.62051/ijcsit.v1n1.01.
Повний текст джерелаАнотація:
With the development of 5G and the maturity of embedded technology, the Internet of Things has become the most promising technology at present, and human motion recognition and fingerprint feature recognition are hot research topics in the Internet of Things. At the same time, the way of human-computer interaction no longer satisfies the interaction only through screens and our computing devices. We hope to achieve human-computer interaction through simpler and more direct operations. For example, gestures, speech, etc., while the current methods of human recognition are mostly achieved through video or wearable devices, both of which have certain limitations. For example, when using video devices to recognize human bodies, it is necessary to ensure that there are no obstacles on the line of sight (LOS) in the video and sufficient ambient light is available. The Wi-Fi recognition method proposed in this article has the advantages of non-wearable, no light source restriction, and the ability to achieve human recognition using non-line-of-sight paths. Wi-Fi is ubiquitous in modern society, so using Wi-Fi signals for indoor human sensing has important research value. Based on existing Wi-Fi gait recognition work, this paper proposes a Wi-Fi-RSMID system for indoor human recognition. By analyzing the channel state information of Wi-Fi signals during human walking, the Wi-Fi-RSMID system extracts key feature information through PCA principal component analysis of thirty subcarriers of CSI. It collects 155 feature points from five principal components of PAC using a method that combines time-frequency domain information, and achieves human recognition through random subspace method. Experiments show that the system can effectively identify the identities of 15 people in different scenarios, with an average recognition rate of about 75.3% - 85.6%.
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JayaSudha, A. R., Pankaj Dadheech, K. Ramalingeswara Prasad, S. Hemalatha, Meghna Sharma, Sajjad Shaukat Jamal, and Daniel Krah. "Intelligent Wearable Devices Enabled Automatic Vehicle Detection and Tracking System with Video-Enabled UAV Networks Using Deep Convolutional Neural Network and IoT Surveillance." Journal of Healthcare Engineering 2022 (March 28, 2022): 1–14. http://dx.doi.org/10.1155/2022/2592365.
Повний текст джерелаАнотація:
The discipline of computer vision is becoming more popular as a research subject. In a surveillance-based computer vision application, item identification and tracking are the core procedures. They consist of segmenting and tracking an object of interest from a sequence of video frames, and they are both performed using computer vision algorithms. In situations when the camera is fixed and the backdrop remains constant, it is possible to detect items in the background using more straightforward methods. Aerial surveillance, on the other hand, is characterized by the fact that the target, as well as the background and video camera, are all constantly moving. It is feasible to recognize targets in the video data captured by an unmanned aerial vehicle (UAV) using the mean shift tracking technique in combination with a deep convolutional neural network (DCNN). It is critical that the target detection algorithm maintains its accuracy even in the presence of changing lighting conditions, dynamic clutter, and changes in the scene environment. Even though there are several approaches for identifying moving objects in the video, background reduction is the one that is most often used. An adaptive background model is used to create a mean shift tracking technique, which is shown and implemented in this work. In this situation, the background model is provided and updated frame-by-frame, and therefore, the problem of occlusion is fully eliminated from the equation. The target tracking algorithm is fed the same video stream that was used for the target identification algorithm to work with. In MATLAB, the works are simulated, and their performance is evaluated using image-based and video-based metrics to establish how well they operate in the real world.
Книги з теми "Wearable video devices in police work"
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Sanchez, Amethyste M. Body cameras in rural law enforcement: Effective crime deterrent or compromising expense. [San Diego, California]: National University, 2015.
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Douglas, Lawrence, Sarat Austin, and Martha Merrill Umphrey. Law and the Visible. University of Massachusetts Press, 2021.
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Douglas, Lawrence, Austin Sarat, and Martha Merrill Umphrey. Law and the Visible. University of Massachusetts Press, 2021.
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Law and the Visible. University of Massachusetts Press, 2021.
Знайти повний текст джерелаЧастини книг з теми "Wearable video devices in police work"
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Drivet, Alessio. "Wearable Cameras." In Wearable Technologies, 489–516. IGI Global, 2018. http://dx.doi.org/10.4018/978-1-5225-5484-4.ch022.
Повний текст джерелаАнотація:
Wearable Technologies represent an emerging theme. Probably the next emerging market where companies will focus. These devices are replacing entire categories of electronic objects in everyday life and affect the way we live, work and socialize. Among the many applications available, the author limits attention to the field of the smart video cameras. This chapter examines some of the most interesting applications of wearable cameras, with special reference to the Italian situation. In particular, the text traces a summary of the main applications in sports, spying, police, army, education, health, disabilities, and lifelogging. A part is devoted to “wearable extensions” and the concept of augmented reality.
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Drivet, Alessio. "Wearable Cameras." In Wearable Technology and Mobile Innovations for Next-Generation Education, 95–121. IGI Global, 2016. http://dx.doi.org/10.4018/978-1-5225-0069-8.ch006.
Повний текст джерелаАнотація:
Wearable Technologies represent an emerging theme. Probably the next emerging market where companies will focus. These devices are replacing entire categories of electronic objects in everyday life and affect the way we live, work and socialize. Among the many applications available, the author limits attention to the field of the smart video cameras. This chapter examines some of the most interesting applications of wearable cameras, with special reference to the Italian situation. In particular, the text traces a summary of the main applications in sports, spying, police, army, education, health, disabilities, and lifelogging. A part is devoted to “wearable extensions” and the concept of augmented reality.
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Bora, Vibha Rajesh, and Bhanu Nagpure. "V-Safe-Anywhere." In Wearable Devices, Surveillance Systems, and AI for Women's Wellbeing, 253–63. IGI Global, 2024. http://dx.doi.org/10.4018/979-8-3693-3406-5.ch015.
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Women's safety is a critical and significant societal concern. Enhancing their safety necessitates a comprehensive strategy that encompasses various facets, including social awareness, educational initiatives, community involvement, and the integration of technological solutions. This chapter introduces an innovative smart IoT device-V-Safe-Anywhere, designed to enhance women's safety in various settings. V-Safe-Anywhere is a wearable device equipped with a camera that captures images periodically while the user is on the move. During unforeseen conditions, the 12 previous instance images which are always stored for security purpose will be sent on server, and video capturing of the scene starts immediately. Using AI, it will detect a face and/or the license plate of a vehicle if it is being used in the crime. Device also sends the real time location of the crime to the guardian and police. The study aims to elucidate its potential impact on women's safety, evaluating its role in both crime prevention and investigation.
Тези доповідей конференцій з теми "Wearable video devices in police work"
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Fortune, Emma, Yaqoub Yusuf, Sarah Zornes, Jorge Loyo Lopez, and Renaldo Blocker. "Assessing Induced Emotions in Employees in a Workplace Setting Using Wearable Devices." In 2020 Design of Medical Devices Conference. American Society of Mechanical Engineers, 2020. http://dx.doi.org/10.1115/dmd2020-9062.
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Abstract A working environment which elicits positive emotions in employees is vital for employee retention, engagement and productivity. Wearable sensors provide the means to objectively measure the emotional responses of employees in the workplace in real-time. The study aim was to perform a preliminary investigation into the validity of two multimodal systems to classify employee’s emotional responses to positive, neutral or negative video stimuli: (1) using wearable electroencephalography (EEG) in combination with video-based facial expression analysis (FEA), and (2) using a wearable galvanic skin response (GSR) device in combination with video-based FEA. Five office employees each watched three short video clips at three time points during their regular work shifts while wearing EEG sensors on the forehead and GSR sensors on the middle and index fingers of their non-dominant hand with their face in view of a webcam. Russel’s circumplex model of affect was used to determine participant’s emotional responses to the video clips. The GSR device showed greater accuracy than the EEG device at detecting arousal responses to the video stimuli, with agreement, precision, and recall values of 87%, 100% and 80%, respectively, compared to 53%, 62%, and 80% for the EEG device. The FEA/EEG and FEA/GSR circumplex models were both able to accurately detect positive emotions elicited from video stimuli with levels of agreement and recall greater than 73%. Precision for the FEA/EEG model to detect positive stimuli was lower due to misclassification of 40% of both negative and neutral stimuli as positive. Precision values for both circumplex models were very low for detecting negative emotions. The results suggest that the EEG and GSR devices may be capable of detecting arousal when used alone, and detecting positive emotions when used in combination with video-based FEA in real-time in the workplace.
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Radu, Catalin, and Sorin Ionitescu. "PERSPECTIVES ON IMPLEMENTING INTERACTIVE ELEARNING TOOLS USING AUGMENTED REALITY IN EDUCATION." In eLSE 2015. Carol I National Defence University Publishing House, 2015. http://dx.doi.org/10.12753/2066-026x-15-015.
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The current rapid advancements in wearable devices hardware and software technologies offer both new opportunities for education, but can also come with challenges for the education providers. Current selling trends among the wearable gadgets indicate a rapid growth in user demand, especially among teenagers and students. The increasing availability of display technologies like OLED and Quantum Dot technologies is opening the doors for the development of new flexible devices, adapting normal surfaces for user interfaces and offering new possibilities of interactivity. The devices' miniaturization, along with the increase of processing power and video cameras performance, can be used as new tools in education, that can add dynamic virtual generated content to information from the real world and provide knowledge on-demand to mobile users. One efficient way to implement such a tool is by using Augmented Reality (AR) software with mobile devices, such as tablets, smartphones of phablets. In this paper, a series of tools are presented, that can be used by teachers and trainers to add 3D generated content by mapping it to objects from the real world, using software tools to process the visual information received from a video camera, analyse the visual content, dynamically add the 3D content depending on the parameters of the real world "markers" and to display the results on the mobile device's screen. The development workflow is also presented, describing the work process from creating a marker to publishing the result as a standalone application that can be installed on an device running Android operating system. The demo application implements these technologies to demonstrate the presented concepts and to provide an example of enhancing a course material in wind energy field by implementing AR to increase the interactivity with the students, increase the self-learning experience and stimulate the students' curiosity and understanding of the technical studied aspects.