Sign Classification Research Articles

The specifics of the subjects of crimes with administrative prejudice in the field of road safety

The subject of this study is the key features of the perpetrators of crimes against road safety, the legal construct of which is made using administrative law. The special status of persons committing crimes of this kind is emphasized by gross disregard for the rules established by law, disregard for the regulations in force against them. Based on the analysis of statistical indicators of road traffic accidents caused by drivers of vehicles, the public danger of committing such criminal acts is justified. These acts imply the onset of criminal consequences only if the person who committed them has special features. The emphasis in this case is on the increased danger of the subjects themselves committing intentional violations of traffic rules, referring them to the malicious type of violators with legal nihilism. To define the concepts of this study, a dialectical methodology was used; in order to comprehensively understand the key features of subjects of crimes with administrative prejudice in the field of road safety, elements of sociological methodology were used. In the study of individual issues, private scientific methods were used, such as formal legal and comparative legal. The results of the conducted research are presented by the classification of signs possessed by the subjects of crimes with administrative prejudice in the field of road safety, their characteristics are given. The connection between the administrative prejudice and the subject as an element of the corpus delicti is substantiated, which, first of all, is justified by the assessment of the social danger of the offender and the choice of a preventive measure commensurate with the social danger of the offender. An important role in assessing public danger is played by determining the psychological portrait of the person who committed the crime, which covers the chronology of his administrative offenses and the frequency of violations of the same prescription. Based on the data obtained about the subject of the crime, the principles of proportionality of punishment and personal responsibility are implemented. In this case, these principles imply the influence of administrative prejudice on the fair distribution of sanctions, and previous administrative offenses aggravate the responsibility of the person.

Self-supervised few-shot learning for real-time traffic sign classification

Although supervised approaches for traffic sign classification have demonstrated excellent performance, they are limited to classifying several traffic signs defined in the training dataset. This prevents them from being applied to different domains, i.e., different countries. Herein, we propose a self-supervised approach for few-shot learning-based traffic sign classification. A center-awareness similarity network is designed for the traffic sign problem and trained using an optical flow dataset. Unlike existing supervised traffic sign classification methods, the proposed method does not depend on traffic sign categories defined by the training dataset. It applies to any traffic signs from different countries. We construct a Korean traffic sign classification (KTSC) dataset, including 6000 traffic sign samples and 59 categories. We evaluate the proposed method with baseline methods using the KTSC, German traffic sign, and Belgian traffic sign classification datasets. Experimental results show that the proposed method extends the ability of existing supervised methods and can classify any traffic sign, regardless of region/country dependence. Furthermore, the proposed approach significantly outperforms baseline methods for patch similarity. This approach provides a flexible and robust solution for classifying traffic signs, allowing for accurate categorization of every traffic sign, regardless of regional or national differences.

Contactless face video based vital signs detection framework for continuous health monitoring using feature optimization and hybrid neural network.

Continuous monitoring of vital signs such as respiration and heart rate is essential to detect and predict conditions that may affect the patient's well-being. To detect these vital signs most medical systems use contact sensors. They are not feasible for long term monitoring and are not repeatable. Vital signs using facial video-noncontact monitoring are becoming increasingly important. Researchers in the last few years although considerable progress has been made, challenging datasets absence timing of assessment process and the technology still has some limitations such as time consuming nature and lack of computer portability. To solve those problems, we propose a contactless video based vital signs detection framework for continuous health monitoring using feature optimization and hybrid neural network. In the proposed technique, modified war strategy optimization algorithm is proposed to segment the face portion from the input video frames. Then, we utilize the known data acquisition models to extract vital signs from the segmented face portions are heart rate, blood pressure, respiratory rate and oxygen saturation. An improved neural network structure (Lifting Net) is further used to achieve the adaptive extraction of deep hidden features for specific signs, for realizing the high precision of human health monitoring. The Hughes effect or dimensionality issue affects detection accuracy in sign classification when there are fewer training instances relative to the number of spectral features. The problem can be overcome through feature optimization here Northern goshawk optimization algorithm is used to select optimal best features which reduces the data dimensionality issue. Furthermore, hybrid deep ensemble reinforcement learning classifier is proposed for the human vital sign detection and classification which ensures the early detection of patient abnormality. Finally, we validate our framework using benchmark video datasets such as TokyoTechrPPG, PURE and COHFACE. To proves the effectiveness of proposed technique using simulation results and comparative analysis.

Концептуальна модель дистинктивної поведінки особистості

B a c k g r o u n d . Modern society is characterized by a variety of individual characteristics manifested in a person's behavior. Distinctiveness is a type of behavior described by non-stereotypical actions, a person's tendency to manifest individuality, and the desire to be socially visible, aiming to create an impression and gain the attention of others. Objective. To develop and disclose a conceptual model of the distinctive behavior of personality. M e t h o d s . Euler-Venn diagrams were used to graphically represent the relationship between distinctive behavior and norms in a particular subject area. R e s u l t s . Two functional dependencies for social and personal factors influencing distinctive behavior are proposed. The concept of "norms", defined from the "angle of view" of the subject area in which a particular norm was formed, is introduced. Two functional dependencies for social and personal factors influencing distinctive behavior are formulated. The concept of norms defined from the "angle of view" of the subject area R is introduced. At the social system level, the forms of distinctiveness manifestation are hypothetically divided. The theory of optimal distinctiveness is considered. The proposed classification of behavioral signs of individual distinctiveness as an element of the social system is divided into three groups, and the main components of each group are highlighted. C o n c l u s i o n s . Through theoretical analysis of the scientific literature, it was determined that distinctive behavior can be described as a type of personality behavior based on the desire to express identity while overcoming the social norms that have developed in a certain reference group in the current period.

Diagnostic criteria of death cause from low temperature in air and water established by traditional expert examination methods

To clarify and supplement the complex of differential diagnostic macro- and microscopic signs of a fatal acute general cold trauma received when person is in the air and water. The conclusions of forensic medical experts on the bodies of people who died from hypothermia in the air and in water (by 150 observations) were analyzed. Methods of descriptive statistics, calculation of the frequency ratio of signs' occurrence were used. The article provides quantitative assessment of occurrence (detection) rate of diagnostically significant signs established with the help of traditional methods of expert examination. A new classification of diagnostic death signs from hypothermia taking into account their differential diagnostic significance and reflecting the conditions of a person's stay in the air and water in the pre-mortem and post-mortem periods, as well as terminal period mechanisms is proposed. The established complexes of signs provide an objective basis for determining death cause in non-obvious conditions when cold exposure is expected to be one of the most damaging factors.

American sign language classification using deep learning

American sign language classification using deep learning

American sign language classification using deep learning

American sign language classification using deep learning

Семантика исторической карты. Вопросы построения и использования условных обозначений для исторических карт

This article is devoted to the methodological and editorial issues of the formation and use of conventional signs when carrying out historical and cartographic work. It indicates the relevance of historical cartographic semantics, which usually does not receive proper reflection in works on the methodology for preparing historical maps. The author formulates the basic requirements for graphical means of the historical map in relation to their syntax, semantic compliance and general readability; provides the methodological basis for constructing and using symbols. Attention is paid to the structure of historical semantics and its individual blocks. A separate block of the article is devoted to the classification of signs and their areas of application. A description of the symbol resources is given. Sources of replenishment of symbols and mechanisms for the formation of new symbols are given. For each geometric type of symbol, an analysis of the various situations in which it can be used is presented; examples are given that reveal the informational and methodological potential of various techniques for using conventional signs. The article clarifies the concept of a reference base of symbols, which is a set of all available signs, grouped by thematic headings. The methodological provisions of the article are based on the practice of general and thematic mapping, as well as on the author’s personal profes- sional experience. The article is supplemented with examples of the thematic legends design, which can be used as a guide for the further accumulation of a reference base of symbols or for direct use when working with historical maps.

Adapting Image Classification Adversarial Detection Methods for Traffic Sign Classification in Autonomous Vehicles: A Comparative Study

Adapting Image Classification Adversarial Detection Methods for Traffic Sign Classification in Autonomous Vehicles: A Comparative Study

Two-Stage Traffic Sign Classification System

Traffic Sign Recognition (TSR) has received widespread attention due to increased traffic accidents caused by a failure to recognize road traffic signs. Most Traffic Sign Recognition remains challenging due to the illumination of natural scenes, and the diversity in traffic signs [31]. To solve the problem, we proposed a two-stage classification system to overcome the above-mentioned challenges and classify the traffic sign boards efficiently. During the first phase, colour based histogram equalizations applied to eliminates the illumination factors and then traffic sign boards are classified into many shapes, which includes circular, triangular, hexagonal, and diamond traffic signs boards based on the modified HOG feature descriptors and various machine learning classifiers are compared [12]. In the second phase, circular and triangular traffic signs are further categorized into specific subclasses using circular and triangular signs trained Convolutional Neural Network (CNN) respectively to overcome intra-class variation among the similar sign board. Experimentation was conducted on German Traffic Sign Recognition Benchmark dataset (GTSRB) [30] and the results show 99.96% top-1 accuracy during the first stage of classification. In the second stage, there is accuracy of 99.40% for circular traffic signs and 97.50% for triangular traffic signs.

Traffic Sign Classification using Deep Learning Comparative Study

In recent years, the advancement of deep learning technologies has significantly impacted various domains and the field of transportation is no exception. As the demand for robust and accurate traffic sign classification systems continues to rise, this study presents an in-depth exploration and comparison of various techniques employed in the field. Focusing on state-of-the-art methodologies, we assess the effectiveness and performance of multiple classification approaches for traffic sign recognition. The review presents an extensive survey of the literature, encompassing traditional computer vision methods, machine learning algorithms, and the latest advancements in deep learning. The comparative analysis aims to identify the strengths and limitations of each technique, considering factors such as computational efficiency and accuracy. Additionally, the paper implements four models—CNN, ResNet50, VGG19, and EfficientNetB7—for traffic sign classification on the GTSRB dataset, the accuracy results are reported as 99.25%, 99.28%, 98.95%, and 98.24% respectively.

Automated sign language detection and classification using reptile search algorithm with hybrid deep learning

Sign language recognition (SLR) contains the capability to convert sign language gestures into spoken or written language. This technology is helpful for deaf persons or hard of hearing by providing them with a way to interact with people who do not know sign language. It is also be utilized for automatic captioning in live events and videos. There are distinct methods of SLR comprising deep learning (DL), computer vision (CV), and machine learning (ML). One general approach utilises cameras for capturing the signer's hand and body movements and processing the video data for recognizing the gestures. One of challenges with SLR comprises the variability in sign language through various cultures and individuals, the difficulty of certain signs, and require for realtime processing. This study introduces an Automated Sign Language Detection and Classification using Reptile Search Algorithm with Hybrid Deep Learning (SLDC-RSAHDL). The presented SLDC-RSAHDL technique detects and classifies different types of signs using DL and metaheuristic optimizers. In the SLDC-RSAHDL technique, MobileNet feature extractor is utilized to produce feature vectors, and its hyperparameters can be adjusted by manta ray foraging optimization (MRFO) technique. For sign language classification, the SLDC-RSAHDL technique applies HDL model, which incorporates the design of Convolutional Neural Network (CNN) and Long-Short Term Memory (LSTM). At last, the RSA was exploited for the optimal hyperparameter selection of the HDL model, which resulted in an improved detection rate. The experimental result analysis of the SLDC-RSAHDL technique on sign language dataset demonstrates the improved performance of the SLDC-RSAHDL system over other existing DL techniques.

Transfer Learning-based Drowsiness Detection System for Driver Assistance and Classification of Traffic Signs Employing a Deep Convolutional Neural Network

In this research paper, a comparative analysis of various image enhancements in the spatial domain techniques was performed based on three-dimensional image quality statistics such as mean squared error (MSE), peak signal-to-noise ratio (PSNR), and structural similarity for measuring the image quality (SSIM). The conditions for choosing the best method are low mean square error (MSE) and high peak signal-to-noise ratio as well as the structural similarity for measuring the image quality. The pre-processed image was subjected to a classification task using a stepwise version of pre-trained deep convolutional neural networks. The problem of driver drowsiness caused by fatigue, lack of sleep, medication, etc. will have to be solved by improving the efficiency of driver drowsiness detection. Many traffic accidents are largely caused by drowsy drivers. Driver fatigue and distraction can cause a lack of alertness, which can lead to driver inattention. In this paper, we proposed a new deep-learning technique for driver drowsiness detection. Two methods using convolutional deep neural network and GoogleNet transfer learning are compared to achieve a better accuracy of 99%.

Traffic Sign Classification Using ASIC

Abstract: The main aim of this project is to focus on traffic signs classification using Tensor flow and deploying the model onto an ASIC for engineering applications. The classification is done using Convolutional Neural Networks (CNNs) in TensorFlow, where the model is trained using a large dataset of traffic sign images. The training images are pre-processed and labelled, and the model is fine-tuned using transfer learning techniques. After the model is trained, it is tested with a separate dataset to ensure its accuracy. Once the model is finalized, it is optimized for deployment onto an ASIC using Micro Python. The ASIC is a custom-designed hardware that is optimized for the specific task of traffic sign classification. The performance of the ASIC is then compared with a standard computer to evaluate its speed and efficiency. This project demonstrates the ability to implement machine learning algorithms onto custom-designed hardware for specific engineering applications, specifically for the purpose of traffic sign classification.

Establishing outcome-driven vital signs ranges for children in the prehospital setting.

Vital signs are frequently used in pediatric prehospital assessments and guide protocol utilization. Common pediatric vital sign classification criteria identify >80% of children in the prehospital setting as having abnormal vital signs, though few receive lifesaving interventions (LSIs). We sought to identify data-driven thresholds for abnormal vital signs by evaluating their association with prehospital LSIs. We evaluated prehospital care records for children (<18 years) transported to the hospital during 2022 from a large, national repository of emergency medical services (EMS) patient encounters. Predictors of interest were heart rate (HR), respiratory rate (RR), systolic blood pressure (SBP), and pulse oximetry. HR, RR, and SBP were converted to Z-scores using age-based distributional models. Our outcome was potential LSIs, defined as performance of selected respiratory procedures, resuscitative interventions, or medication administrations. Using cut point analysis, we identified higher specificity (maximal specificity with a minimum of 25% sensitivity) and higher sensitivity (maximal sensitivity with a minimum of 25% specificity) ranges for each vital sign and evaluated measures of diagnostic accuracy. We included 987,515 children (median age 10 years, IQR 2-15 years). An LSI occurred in 4.3% (2.1% with respiratory procedures, 1.2% with resuscitative interventions, and 2.0% with medication administration). HR, RR, and SBP demonstrated a U-shaped association with LSIs. Specificities ranged from 84.1% to 93.7% for higher specificity criteria, with RR demonstrating the best performance (sensitivity 84.6%, specificity 27.0%). Sensitivities ranged from 62.3% to 84.4% for higher sensitivity criteria. Cut points for pediatric vital signs were associated with LSIs. Specific age-adjusted ranges can identify children at higher and lower risk for receipt of LSI. These ranges may be combined with other objective measures to improve the assessment of children in the prehospital setting, assist in optimizing protocol utilization, improve transport decision making, and guide destination selection.

Certified Control for Train Sign Classification

Certified Control for Train Sign Classification

Constraints on Optimising Encoder-Only Transformers for Modelling Sign Language with Human Pose Estimation Keypoint Data.

Supervised deep learning models can be optimised by applying regularisation techniques to reduce overfitting, which can prove difficult when fine tuning the associated hyperparameters. Not all hyperparameters are equal, and understanding the effect each hyperparameter and regularisation technique has on the performance of a given model is of paramount importance in research. We present the first comprehensive, large-scale ablation study for an encoder-only transformer to model sign language using the improved Word-level American Sign Language dataset (WLASL-alt) and human pose estimation keypoint data, with a view to put constraints on the potential to optimise the task. We measure the impact a range of model parameter regularisation and data augmentation techniques have on sign classification accuracy. We demonstrate that within the quoted uncertainties, other than ℓ2 parameter regularisation, none of the regularisation techniques we employ have an appreciable positive impact on performance, which we find to be in contradiction to results reported by other similar, albeit smaller scale, studies. We also demonstrate that the model architecture is bounded by the small dataset size for this task over finding an appropriate set of model parameter regularisation and common or basic dataset augmentation techniques. Furthermore, using the base model configuration, we report a new maximum top-1 classification accuracy of 84% on 100 signs, thereby improving on the previous benchmark result for this model architecture and dataset.

Enhancing Road Safety with Convolutional Neural Network Traffic Sign Classification

Recent computer vision and deep learning breakthroughs have improved road safety by automatically classifying traffic signs. This research uses CNNs to classify traffic signs to improve road safety. Autonomous vehicles and intelligent driver assistance systems require accurate traffic sign detection and classification. Using deep learning, we created a CNN model that can recognize and classify road traffic signs. This research uses a massive dataset of labeled traffic sign photos for training and validation. These CNN algorithms evaluate images and produce real-time predictions to assist drivers and driverless cars in understanding traffic signs. Advanced driver assistance systems, navigation systems, and driverless vehicles can use this technology to give drivers more precise information, improving their decision-making and road safety. Researcher optimized CNN model design, training, and evaluation metrics during development. The model was rigorously tested and validated for robustness and classification accuracy. The research also solves real-world driving obstacles like illumination, weather, and traffic signal obstructions. This research shows deep learning-based traffic sign classification can dramatically improve road safety. This technology can prevent accidents and enhance traffic management by accurately recognizing and interpreting traffic signs. It is also a potential step toward a safer, more efficient transportation system with several automotive and intelligent transportation applications. Road safety is a global issue, and CNN-based traffic sign classification can reduce accidents and improve driving. On filter 3, Convolutional Neural Network training accuracy reached 98.9%, while validation accuracy reached 88.23%.

Traffic sign attack via pinpoint region probability estimation network

Recent work show that Deep Neural Networks (DNNs) have created great performance in many tasks, but they are vulnerable to adversarial examples which trigger Artificial Intelligence (AI) security risks. Especially in the autonomous driving field, attacking a traffic sign classification network results in a serious consequence. Most existing researches prefer to digital level attacks focusing on smaller or more imperceptible adversarial noise. Given that attacks available to real-world implementation usually emerge in more security-critical scenarios, we propose an adaptively adversarial example generation algorithm for physical attacks in the real-world setting. Taking account of the traffic sign classification, our approach is divided into two steps. The first step is to generate a probability map which precisely predicts the probability of being attacked for each pixel in the input image through the proposed Pinpoint Region Probability Estimation Network (PRPEN) and meanwhile, try to reduce the size of the highlighted area in the map. It can also be regarded as a classification problem in which every pixel has two classes, suitable for attacking or not, including the restrictions on the number of items in the suitable sort. The second one is to remake a mask depending on the probability map and optimize adversarial patches only on what mask decides. Experimental results show that our method achieves almost 100% misclassification rate in several widely used networks with even smaller patches. We also find how to effectively disguise as a target class to mislead the DNN classifiers and improve AI security.

A Deep Neural Network Attack Simulation against Data Storage of Autonomous Vehicles

&lt;div&gt;In the pursuit of advancing autonomous vehicles (AVs), data-driven algorithms have become pivotal in replacing human perception and decision-making. While deep neural networks (DNNs) hold promise for perception tasks, the potential for catastrophic consequences due to algorithmic flaws is concerning. A well-known incident in 2016, involving a Tesla autopilot misidentifying a white truck as a cloud, underscores the risks and security vulnerabilities. In this article, we present a novel threat model and risk assessment (TARA) analysis on AV data storage, delving into potential threats and damage scenarios. Specifically, we focus on DNN parameter manipulation attacks, evaluating their impact on three distinct algorithms for traffic sign classification and lane assist. Our comprehensive tests and simulations reveal that even a single bit-flip of a DNN parameter can severely degrade classification accuracy to less than 10%, posing significant risks to the overall performance and safety of AVs. Additionally, we identify critical parameters based on bit position, layer position, and bit-flipping direction, offering essential insights for developing robust security measures in autonomous vehicle systems.&lt;/div&gt;