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Development on Autonomous Object Tracker Robot using Raspberry Pi
... It has numerous applications [19], which are all unique from one another since it may be applied to secure motion capture cameras, AI assistants, low-power smartphones, home automation, or laboratory equipment, and teach bioinformatics [25]. It is frequently used for discovering objects [26], monitoring the environment, and speed, and designing paths [27] [28][29] [30].The Raspberry Pi model 4B, which is currently available and has a remote server display, can further expand the sophisticated requirements for transmission and monitoring in real-time, even through video with high resolution. Authors [31] have developed a brand-new haze metric called "SATVAL," which evaluates the ratio of an RGB image's maximum saturation to its maximum value when applied to an image scattering model and analyzed in a few moments of video. ...
Real-time plays a pivotal role in diverse scientific application, and optimizing the measurement process is essential to enhance efficiency. This study proposes the implementation of geometric methods to optimize real-time measurements through image processing. By evaluating the available space for a mobile robot to move, indirect measurement of the object as an obstacle in real time is achieved. This technique effectively reduces data transmission speed, a critical parameter for real-time measurements, with the potential to lower costs by necessitating smaller and simpler technical specifications for equipment. In order to enhance measurement accuracy, stereo cameras capturing images from various angles are employed simultaneously. However, this may introduce complexities in calculations, such as parallax errors. To address this issue, filters and restoration methods are applied, such as Gauss, to correct noise and accurately measure space. The study presents real-time results for a static object using the dynamic system, which demonstrated an accuracy exceeding 99%. By utilizing the same image for different calculations, the system complexity is minimized, also measurement time is reduced. While current optimization focuses on 2D, there is potential for extension in 3D. The methodologies presented in this study primarily involve analytical approaches such as Distance-Based Optimization and Multicriteria Decision Analytics. The study's findings and proposed technology pave the way for exciting future research and practical implementations, potentially revolutionizing real-time measurements in numerous scientific and technology domains.
The development of hydroelectric power is essential to the production of sustainable energy. Continuous monitoring and maintenance are necessary to guarantee the safe and effective functioning of hydroelectric plants. A revolutionary method for improving the security, effectiveness, and monitoring capacities of hydroelectric power plants is presented in the project "Monitoring through AI-Based Remote Access Vehicle in Hydro Power Plant."This creative project uses cutting-edge technologies, such as Bluetooth connectivity, artificial intelligence (AI), Raspberry Pi, and sensor systems, to develop a remotely controlled car that is specifically designed to meet the demands of hydroelectric power plant conditions. Real-time object detection and obstacle avoidance are made possible by this configuration, guaranteeing safe passage across the complex industrial infrastructure.Incorporating a temperature sensor also makes it easier to identify fire situations early on, while a water-detecting sensor protects against water leaks. By utilizing AI algorithms for object recognition, vehicles can detect and react to impediments in a proactive manner, hence decreasing the likelihood of accidents. As sentinels, the temperature and water leakage sensors keep an eye on things constantly and send out alarms right once if anything seems out of the ordinary. Reduced reliance on in-person inspections due to remote monitoring capabilities results in lower costs and more dependability. To sum up, this initiative tackles important issues pertaining to hydro power plant maintenance, efficiency, and safety
Various aspects of the human workplace have been influenced by robotics due to its precision and accessibility. Nowadays, industrial activities have become more automated, increasing efficiency while reducing the production time, human labor, and risks involved. With time, electronic technology has advanced, and the ultimate goal of such technological advances is to make robotic systems as human-like as possible. As a result of this blessing of technological advances, robots will perform jobs far more efficiently than humans in challenging situations. In this paper, an automatic computer vision-based robotic gripper has been built that can select and arrange objects to complete various tasks. This study utilizes the image processing methodology of the PixyCMU camera sensor to distinguish multiple objects according to their distinct colors (red, yellow, and green). Next, a preprogrammed command is generated in the robotic arm to pick the item employing Arduino Mega and four MG996R servo motors. Finally, the device releases the object according to its color behind the fixed positions of the robotic arm to a specific place. The proposed system can also detect objects' geometrical shapes (circle, triangle, square, rectangle, pentagon, and star) and sizes (large, medium, and small) by utilizing OpenCV image processing libraries in Python language. Empirical results demonstrate that the designed robotic arm detects colored objects with 80% accuracy. It performs an excellent size and shapes recognition precision in real-time with 100% accuracy.
Smart surveillance systems are used to monitor specific areas, such as homes, buildings, and borders, and these systems can effectively detect any threats. In this work, we investigate the design of low-cost multiunit surveillance systems that can control numerous surveillance cameras to track multiple objects (i.e., people, cars, and guns) and promptly detect human activity in real time using low computational systems, such as compact or single board computers. Deep learning techniques are employed to detect certain objects to surveil homes/buildings and recognize suspicious and vital events to ensure that the system can alarm officers of relevant events, such as stranger intrusions, the presence of guns, suspicious movements, and identified fugitives. The proposed model is tested on two computational systems, specifically, a single board computer (Raspberry Pi) with the Raspbian OS and a compact computer (Intel NUC) with the Windows OS. In both systems, we employ components, such as a camera to stream real-time video and an ultrasonic sensor to alarm personnel of threats when movement is detected in restricted areas or near walls. The system program is coded in Python, and a convolutional neural network (CNN) is used to perform recognition. The program is optimized by using a foreground object detection algorithm to improve recognition in terms of both accuracy and speed. The saliency algorithm is used to slice certain required objects from scenes, such as humans, cars, and airplanes. In this regard, two saliency algorithms, based on local and global patch saliency detection are considered. We develop a system that combines two saliency approaches and recognizes the features extracted using these saliency techniques with a conventional neural network. The field results demonstrate a significant improvement in detection, ranging between 34% and 99.9% for different situations. The low percentage is related to the presence of unclear objects or activities that are different from those involving humans. However, even in the case of low accuracy, recognition and threat identification are performed with an accuracy of 100% in approximately 0.7 s, even when using computer systems with relatively weak hardware specifications, such as a single board computer (Raspberry Pi). These results prove that the proposed system can be practically used to design a low-cost and intelligent security and tracking system.
“Hello, I’m the TERMINATOR, and I’ll be your server today”. Diners might soon be feeling this greeting, with Optimus Prime in the kitchen and Wall-E then sending your order to C-3PO. In our daily lives, a version of that future is already showing up. Robotics companies are designing robots to handle tasks, including serving, interacting, collaborating, and helping. These service robots are intended to coexist with humans and engage in relationships that lead them to a better quality of life in our society. Their constant evolution and the arising of new challenges lead to an update of the existing systems. This update provides a generic vision of two questions: the advance of service robots, and more importantly, how these robots are applied in society (professional and personal) based on the market application. In this update, a new category is proposed: catering robotics. This proposal is based on the technological advances that generate new multidisciplinary application fields and challenges. Waiter robots is an example of the catering robotics. These robotic platforms might have social capacities to interact with the consumer and other robots, and at the same time, might have physical skills to perform complex tasks in professional environments such as restaurants. This paper explains the guidelines to develop a waiter robot, considering aspects such as architecture, interaction, planning, and execution.
Using robot cars has been the best choice for surveillance of the unrecognized area. This project proposed a prototype of Wireless Indoor Surveillance Robot Car using Raspberry Pi to enable real time video surveillance and which controlled by smartphone or laptop. From the test, the monitor is able to capture real time image and also detect the battery remaining power which saves the management cost.
1 1 This work is part of a line of research known as Terminology, developed by the Program in Linguistic Studies at Universidade Estadual Paulista - UNESP / São José do Rio Preto, SP.
Terminology; Bilingual Terminological Dictionary; Microstructure; Real Estate Lease Agreements
This paper describes implementation of target detecting and tracking system, which can extract required information from images without need for an external processing unit and track the target, based on embedded system. The requirements of image capturing, target detection, allocate target position, and implementing based on raspberry pi system as embedded system on chip kit. Real time tracking is achieved using simple and fast color detection procedures based on frame differencing and camera motion compensation for estimate the motion vectors to control the pan tilt DC motors, which drive the camera to track the moving target in real-time.
Summary Points
• During the past year, the United States has experienced major controversies—and civil unrest—regarding the endemic problem of police violence and police deaths.
• Although deaths of police officers are well documented, no reliable official US data exist on the number of persons killed by the police, in part because of long-standing and well- documented resistance of police departments to making these data public.
• These deaths, however, are countable, as evidenced by “The Counted,” a website launched on June 1, 2015, by the newspaper The Guardian, published in the United Kingdom, which quickly revealed that by June 9, 2015, over 500 people in the US had been killed by the police since January 1, 2015, twice what would be expected based on estimates from the US Federal Bureau of Intelligence (FBI).
• Law-enforcement–related deaths, of both persons killed by law enforcement agents and also law enforcement agents killed in the line of duty, are a public health concern, not solely a criminal justice concern, since these events involve mortality and affect the well- being of the families and communities of the deceased; therefore, law-enforcement– related deaths are public health data, not solely criminal justice data.
• We propose that law-enforcement–related deaths be treated as a notifiable condition, which would allow public health departments to report these data in real-time, at the local as well as national level, thereby providing data needed to understand and prevent the problem.
The surveillance in defense areas and military borders has been an important and essential factor for ensuring the safety of the people. This paper helps to build a prototype that might also help to get unreachable locations like dense forests and also places such as mines. This spying vehicle can be used for surveillance in any of the places required. The wireless technology Wi-Fi is used to efficiently monitor the area and to control the movement robotic vehicle. The robot is designed in such a way that it can be wirelessly controlled by the user using a Wi-Fi technology, and it can provide efficient surveillance with the help of sensor and facial recognition feature. A PIR sensor is used for the detection of any person or obstacles. A camera is interfaced with the robotic system to capture the face of the person and identify the intruder. A face recognition algorithm is used to identify the intruding person. Thus, the developed robot results in providing security in remote location while enabling the benefit of considerable reduction in loss of people.KeywordsRaspberry PiNight vision cameraSurveillanceMotor driverPIR sensorWi-FiFace detectionFacial recognitionVNC viewer
One of the fast-growing areas of deep learning using artificial intelligence is computer vision, becoming increasingly popular. It is a growing field of research that seeks to create techniques to help computers “see” and recognize the information of digital images for instance videos and photographs. Object detection is a computer vision method that enables us to recognize objects in an image or video and locate them. This article describes an efficient shape-based object identification method and its displacement in real-time using OpenCV library of programming roles mostly targeted at computer vision and Raspberry Pi with camera module.
Nowadays, internet has become an important part of everyone's lives. The internet has turned out to be a reliable tool for communication, data transfer and many more applications for people from all over the world. An individual's internet activities are being tracked and traced without their knowledge by various means. Thus, online security is a genuine worry for web clients who intend to visit interpersonal interaction locales, make an online transaction, or take an interest in internet diversions, as they need their protection to be ensured. Internet browsing is not anonymized as many Internet Service Providers (ISP's) could legally sell users information without informing users. People's internet activities are being tracked and traced by ISPs. Moreover, network spies and snooping attempts are very hard to be defeated as an ample network snooping tools are available in the market. These issues can be curbed by using a router that has anonymous browsing infrastructure. However, most of the routers that gives anonymous browsing are not portable. From this project, people will be able to browse anonymously without being traced. Besides that, network snooping and spying attempts can be defeated. Finally, a portable The Onion Routing (TOR) router can be created with a Raspberry Pi..
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Bilingual Terminological Dictionary Of Real Estate Lease Agreements: Development Of Translator-Oriented Microstructure
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