This paper presents a technique for motion detection that incorporates several innovative mechanisms. For example, our proposed technique stores, for each pixel, a set of values taken in the past at ...the same location or in the neighborhood. It then compares this set to the current pixel value in order to determine whether that pixel belongs to the background, and adapts the model by choosing randomly which values to substitute from the background model. This approach differs from those based upon the classical belief that the oldest values should be replaced first. Finally, when the pixel is found to be part of the background, its value is propagated into the background model of a neighboring pixel. We describe our method in full details (including pseudo-code and the parameter values used) and compare it to other background subtraction techniques. Efficiency figures show that our method outperforms recent and proven state-of-the-art methods in terms of both computation speed and detection rate. We also analyze the performance of a downscaled version of our algorithm to the absolute minimum of one comparison and one byte of memory per pixel. It appears that even such a simplified version of our algorithm performs better than mainstream techniques.
Traffic accident may occur at any time, and most of the time is because while driving a car, the driver is tired. Road wreck can be prevented if pilot of the vehicle is conscious of the state and ...circumstance of caution on the lane. There are many technologies for development today, one of which is Technique of detecting the objects. The technology for detecting the object requires to detect a detailed facial recognition. Researchers develop a system that is capable of detecting the drowsiness to alert the pilot when the pilot appears sleepy while driving a vehicle, this may avoid crash. Pilot that is the consumer in this case, is they shout-off the eye in seconds, the sensors that is the smartphone's front camera can catch and analyze this occurrence and then activate the device to warn the user to speech. Authors use OpenCV Library to process the detected file. OpenCV Library uses the Haar Cascade Classifier to classify features like face and eyes. In this method, the eye and facial recognition are targetted. On the Android Operating System, this program will be introduced.
Abstract— The purpose of this paper is to show the design technique and programming of an autonomous robot prototype that can go to a predetermined location and return to its starting place utilizing ...the Global Positioning System (GPS). An HD camera along with an ultrasonic sensor and two IR sensors is used to provide necessary data from the real world to the robot to avoid obstructions, by following GPS Waypoints. A smart algorithm was introduced for path planning and re-planning. The robot can modify its direction using a digital compass reading and input from sensors by computing the heading angle from the current GPS point. This self-navigation mobile robot's accuracy was evaluated in several locations and is quantified in terms of heading angle and path length from the starting point to the goal point.
•Creating new dataset (ASLA) for American Sign Language alphabet recognition which help many searchers.•Suggesting good deep learning model for sign language recognition model.•Comparing the ...performance of the suggested model on our own dataset and other datasets.•Designing sign language recognition interface.
Sign language is an essential means of communication for hearing-impaired individuals.
We aimed to develop an American sign language recognition dataset and use it in the deep learning model which depends on neural networks to interpret gestures of sign language and hand poses to natural language.
In this study, we developed a dataset and a Convolutional Neural Network-based sign language interface system to interpret gestures of sign language and hand poses to natural language. The neural network developed in this study is a Convolutional Neural Network (CNN) which enhances the predictability of the American Sign Language alphabet (ASLA). This research establishes a new dataset of the American Sign Language alphabet which takes into consideration various conditions such as lighting and distance.
The dataset created in this study is a new addition in the field of sign language recognition (SLR). This dataset may be used to develop SLR systems. Furthermore, our research compares the results of our dataset with two different datasets from other studies. The other datasets have invariant scene conditions, but our suggested CNN model demonstrated high accuracy for all the tested datasets. Despite the different conditions and volume of the new dataset, it achieved 99.38% accuracy with excellent prediction and small loss (0.0250).
The proposed system may be considered a promising solution in medical applications that use deep learning with superior accuracy. Moreover, our dataset was created under variable conditions which increases the number of contributions, comparisons, results and conclusions in the field of SLR and may enhance such systems.
Perkembangan zaman modern di sektor teknologi robotika dan mekanisasi telah meningkat sangat signifikan dalam beberapa dekade ini karena efisiensinya yang tinggi dari aspek waktu dan tenaga. Pada ...sistem mobilisasi barang untuk pemanfaatan perusahaan, khususnya bagian industri dan bagian pergudangan, salah satu robot yang digunakan adalah kendaraan berpemandu otomatis pengangkut barang atau yang biasa disebut sebagai automatic guided vehicle (AGV). Salah satu metode navigasi lama di AGV adalah penggunaan sebuah sensor untuk mengikuti pola garis pada objek yang terdeteksi, yaitu garis pada lantai. Metode tersebut kurang efektif karena lambat laun objek pola garis di lantai tersebut akan menghilang akibat efek dari gaya gesek roda AGV, sehingga tidak lagi dapat terdeteksi oleh sensor kamera. Oleh sebab itu, diperlukan sebuah peningkatan metode navigasi AGV agar dapat menjadi sebuah inovasi yang berkelanjutan. Metode navigasi ini menggunakan empat objek gambar yang diposisikan pada area yang dilintasi robot AGV dan kamera sebagai sensor yang menghadap ke depan, sehingga AGV dapat dengan presisi mendeteksi pola objek gambar menggunakan bantuan computer vision menggunakan library perangkat lunak OpenCV. Selanjutnya, pola objek gambar yang sudah terdeteksi diproses oleh sebuah program yang dirancang pada perangkat komputer mini Raspberry Pi 4 Model B. Hasil pengujian membuktikan bahwa metode ini mampu mendeteksi objek gambar yang berada di area yang terjangkau kamera dan berhasil menampilkan keluaran dari objek gambar tersebut. Sistem berhasil mengenali objek secara cukup akurat, dengan parameter jarak 10–95 cm dan melalui beberapa percobaan. Analisis kecepatan putaran roda depan dan belakang AGV dilakukan menggunakan osiloskop dan takometer sebagai alat pengukur kecepatan atau rotasi roda.
•Simultaneous imaging, analysis, and fabrication is the future of TEM.•Computer vision is used to identify nanopores in TEM images of 2D WS2 membranes.•Gradient ascent/descent is 1000x faster than ...grid search to optimize parameters.•Mask R-CNN methods were unfit in this case given insufficient data.
Transmission electron microscopy (TEM) has led to important discoveries in atomic imaging and as an atom-by-atom fabrication tool. Using electron beams, atomic structures can be patterned, annealed and crystallized, and nanopores can be drilled in thin membranes. We review current progress in TEM analysis and implement a computer vision nanopore-detection algorithm that achieves a 96% pixelwise precision in TEM images of nanopores in 2D membranes (WS2), and discuss parameter optimization including a variation on the traditional grid search and gradient ascent. Such nanopores have applications in ion detection, water filtration, and DNA sequencing, where ionic conductance through the pore should be concordant with its TEM-measured size. Standard computer vision methods have their advantages as they are intuitive and do not require extensive training data. For completeness, we briefly comment on related machine learning for 2D materials analysis and discuss relevant progress in these fields. Image analysis alongside TEM allows correlated fabrication and analysis done simultaneously in situ to engineer devices at the atomic scale.
In the era of information system authentication is a major problem. Automated embedded systems in today’s world have made a lot of progress. The importance of an automated embedded system has proved ...highly effective in applications such as surveillance and private security. Modern smart door locks are very susceptible to errors and damage, which will reduce security. Almost every intelligent Door lock has a passcode entry or faces recognition outside the door which makes it vulnerable. The paper is intended to provide the user with open source software OpenCV and proposed an Efficient attitude tracking algorithm (EATA). Furthermore, this article aims to ensure that a key lock system that is retro and modern simultaneously offers a certain safety and reliability. The experimental results show that the proposed system is more efficient, consumes less power, and cost-effective.
Augmented reality applications related with faces such as make-up, hair design, wearing glasses are mostly prepared for entertainment purposes. Facilitating the preparation of augmented reality ...applications and more accurate analysis of real-world data in applications will enable these applications to be used more widely in different sectors such as R&D, education and marketing. In generally, the steps in image-based augmented reality applications can be listed as follows; detection of the targeted object, finding two reference points for each targeted object in 2D images, determining the boundaries of virtual object in its image and inserting the virtual object in real time. In this study, the problems that may be encountered in preparations of these augmented reality applications expected to be used more in the future are examined through a case study. Firstly, haar cascade classifiers, used to find different face areas, are compared and as a result of the comparison, it is decided to use eye haar cascade. Afterwards, rule-based approaches have been used to eliminate the wrong ones among the found eyes and to match the eyes of the same face. Then the position, size and angle of the virtual object to be added are calculated and it is added to the face using affine transformations. The problems encountered in augmented reality and algorithms used for problem solving are explained through the virtual hat application, but these simply prepared algorithms, can be used for different objects such as hair and glasses by changing the target points.
Despite advanced construction technologies that are unceasingly filling the city-skylines with glassy high-rise structures, maintenance of these shining tall monsters has remained a high-risk ...labor-intensive process. Thus, nowadays, utilizing façade-cleaning robots seems inevitable. However, in case of navigating on cracked glass, these robots may cause hazardous situations. Accordingly, it seems necessary to equip them with crack-detection system to eventually avoid cracked area. In this study, benefitting from convolutional neural networks developed in TensorFlow™, a deep-learning-based crack detection approach is introduced for a novel modular façade-cleaning robot. For experimental purposes, the robot is equipped with an on-board camera and the live video is loaded using OpenCV. The vision-based training process is fulfilled by applying two different optimizers utilizing a sufficiently generalized data-set. Data augmentation techniques and also image pre-processing also apply as a part of process. Simulation and experimental results show that the system can hit the milestone on crack-detection with an accuracy around 90%. This is satisfying enough to replace human-conducted on-site inspections. In addition, a thorough comparison between the performance of optimizers is put forward: Adam optimizer shows higher precision, while Adagrad serves more satisfying recall factor, however, Adam optimizer with the lowest false negative rate and highest accuracy has a better performance. Furthermore, proposed CNN's performance is compared to traditional NN and the results provide a remarkable difference in success level, proving the strength of CNN.
•Automatic glass crack detection is proposed for a novel façade-cleaner robot (Mantis).•CNN-based deep learning is used to replace human inspection in glass crack detection.•A broad data-set is collected with several types of object reflections and glare effect.•Hitting an accuracy of around 90% in recognizing cracked glass, is a great milestone.•Sufficient quantitative metrics are provided for comparing the two optimizers applied.
The way Data Science and Machine Learning have set modern trends for automation. It is thoughtful to simplify our day to day activities which are related to these domains. One such task is the ...repetitive submission of the same personal details while submitting any online form regarding academic or other purposes. The repetitiveness seems a bit dilatory. Plus, the involvement of machine learning further espouses the concept to automate re-iteration of personal details each time we fill a form, instead of manual entry.
To obviate the above redundancy, we have proposed to implement “Exam Form Automation Using Facial Recognition” which includes real time face recognition with the help of webcam, pre-storing data on web servers with Panda and NumPy library of Python and then automating the entries on form using selenium library of python. The system captures a real time image of a user and collects the particular details from data hosted on a web server. Thus, precluding the monotonous procedure of manual entry.
PDF
