Road Sign Analysis Using Multisensory Data

This paper deals with the problem of estimating the following road sign parameters: height, dimensions, visibility distance and partial occlusions. This work belongs to a framework whose main applications involve road sign maintenance, driver assistance, and inventory systems. From this paper we suggest a multisensory system composed from two cameras, a GPS receiver, and a distance measurement device,all of them installed in a car. The process consists of several steps which include road sign detection, recognition and tracking , and road signs parameters estimation. From some trigonometric properties, and a camera model, the information provided by the tracking subsystem and the distance measurement sensors, we estimate the road signs parameters.Results show that the described calculation methodology offers a correct estimation for all types of traffic signs

Road Sign Analysis Using Multisensory Data

This paper deals with the problem of estimating the following road sign parameters: height, dimensions, visibility distance and partial occlusions. This work belongs to a framework whose main applications involve road sign maintenance, driver assistance, and inventory systems. From this paper we suggest a multisensory system composed from two cameras, a GPS receiver, and a distance measurement device,all of them installed in a car. The process consists of several steps which include road sign detection, recognition and tracking , and road signs parameters estimation. From some trigonometric properties, and a camera model, the information provided by the tracking subsystem and the distance measurement sensors, we estimate the road signs parameters.Results show that the described calculation methodology offers a correct estimation for all types of traffic signs

Real-time accumulative computation motion detectors

The neurally inspired accumulative computation (AC) method and its application to motion detection have been introduced in the past years. This paper revisits the fact that many researchers have explored the relationship between neural networks and finite state machines. Indeed, finite state machines constitute the best characterized computational model, whereas artificial neural networks have become a very successful tool for modeling and problem solving. The article shows how to reach real-time performance after using a model described as a finite state machine. This paper introduces two steps towards that direction: (a) A simplification of the general AC method is performed by formally transforming it into a finite state machine. (b) A hardware implementation in FPGA of such a designed AC module, as well as an 8-AC motion detector, providing promising performance results. We also offer two case studies of the use of AC motion detectors in surveillance applications, namely infrared-based people segmentation and color-based people tracking, respectively

An optimization on pictogram identification for the road-sign recognition task using svms

Pattern recognition methods are used in the final stage of a traffic sign detection and recognition system, where the main objective is to categorize a detected sign. Support vector machines have been reported as a good method to achieve this main target due to their ability to provide good accuracy as well as being sparse methods. Nevertheless, for complete data sets of traffic signs the number of operations needed in the test phase is still large, whereas the accuracy needs to be improved. The objectives of this work are to propose pre-processing methods and improvements in support vector machines to increase the accuracy achieved while the number of support vectors, and thus the number of operations needed in the test phase, is reduced. Results show that with the proposed methods the accuracy is increased 3?5% with a reduction in the number of support vectors of 50?70%

A “Salt and Pepper” Noise Reduction Scheme for Digital Images Based on Support Vector Machines Classification and Regression

We present a new impulse noise removal technique based on Support Vector Machines (SVM). Both classification and regression were used to reduce the “salt and pepper” noise found in digital images. Classification enables identification of noisy pixels, while regression provides a means to determine reconstruction values. The training vectors necessary for the SVM were generated synthetically in order to maintain control over quality and complexity. A modified median filter based on a previous noise detection stage and a regression-based filter are presented and compared to other well-known state-of-the-art noise reduction algorithms. The results show that the filters proposed achieved good results, outperforming other state-of-the-art algorithms for low and medium noise ratios, and were comparable for very highly corrupted images

Assistive Robot with an AI-Based Application for the Reinforcement of Activities of Daily Living: Technical Validation with Users Affected by Neurodevelopmental Disorders

In this work, we propose the first study of a technical validation of an assistive robotic platform, which has been designed to assist people with neurodevelopmental disorders. The platform is called LOLA2 and it is equipped with an artificial intelligence-based application to reinforce the learning of daily life activities in people with neurodevelopmental problems. LOLA2 has been integrated with an ROS-based navigation system and a user interface for healthcare professionals and their patients to interact with it. Technically, we have been able to embed all these modules into an NVIDIA Jetson Xavier board, as well as an artificial intelligence agent for online action detection (OAD). This OAD approach provides a detailed report on the degree of performance of a set of daily life activities that are being learned or reinforced by users. All the human–robot interaction process to work with users with neurodevelopmental disorders has been designed by a multidisciplinary team. Among its main features are the ability to control the robot with a joystick, a graphical user interface application that shows video tutorials with the activities to reinforce or learn, and the ability to monitor the progress of the users as they complete tasks. The main objective of the assistive robotic platform LOLA2 is to provide a system that allows therapists to track how well the users understand and perform daily tasks. This paper focuses on the technical validation of the proposed platform and its application. To do so, we have carried out a set of tests with four users with neurodevelopmental problems and special physical conditions under the supervision of the corresponding therapeutic personnel. We present detailed results of all interventions with end users, analyzing the usability, effectiveness, and limitations of the proposed technology. During its initial technical validation with real users, LOLA2 was able to detect the actions of users with disabilities with high precision. It was able to distinguish four assigned daily actions with high accuracy, but some actions were more challenging due to the physical limitations of the users. Generally, the presence of the robot in the therapy sessions received excellent feedback from medical professionals as well as patients. Overall, this study demonstrates that our developed robot is capable of assisting and monitoring people with neurodevelopmental disorders in performing their daily living tasks.This research was funded by project AIRPLANE, with reference PID2019-104323RB-C31, of Spain’s Ministry of Science and Innovation

Assessment and counseling to get the best efficiency and effectiveness of the assistive technology (MATCH): study protocol

Clinical Trials ID: NCT04723784[Abstract] Aims: To determine the psychosocial impact of assistive technology(AT) based on robotics and artificial intelligence in the life of people with disabilities. Background: The best match between any person with disabilities and its AT only can be gotten through a complete assessment and monitoring of his/her needs, abilities, priorities, difficulties and limitations. Without this analysis, it's possible that the device won't meet the individual's expectations. Therefore, it is important that any project focused on the development of innovating AT for people with disabilities includes the perspective of outcome measures as an important phase of the research. In this sense, the integration of the assessment, implementation process and outcome measures is crucial to guarantee the transferability for the project findings and to get the perspective from the final user. Methods: Pilot study, with prospective, longitudinal and analytical cohort. The study lasts from July 2020 until April 2023. The sample is formed by people with disabilities, ages from 2-21, that will participate from the first stage of the process (initial assessment of their abilities and needs) to the final application of outcome measures instruments (with a complete implication during the test of technology). Discussion: Only with the active participation of the person is possible to carry out a user-centered approach. This fact will allow us to define and generate technological solutions that really adjust to the expectations, needs and priorities of the people with disabilities, avoiding the AT from being abandoned, with the consequent health and social spending.Ministerio de Ciencia e Innovación (España); PID2019-104323RB-C31Ministerio de Ciencia e Innovación (España); PID2019-104323RB-C32Ministerio de Ciencia e Innovación (España); PID2019-104323RB-C33Ministerio de Ciencia e Innovación (España); PID2019-104323RB-C3

Importance Sampling for Objetive Funtion Estimations in Neural Detector Traing Driven by Genetic Algorithms

To train Neural Networks (NNs) in a supervised way, estimations of an objective function must be carried out. The value of this function decreases as the training progresses and so, the number of test observations necessary for an accurate estimation has to be increased. Consequently, the training computational cost is unaffordable for very low objective function value estimations, and the use of Importance Sampling (IS) techniques becomes convenient. The study of three different objective functions is considered, which implies the proposal of estimators of the objective function using IS techniques: the Mean-Square error, the Cross Entropy error and the Misclassification error criteria. The values of these functions are estimated by IS techniques, and the results are used to train NNs by the application of Genetic Algorithms. Results for a binary detection in Gaussian noise are provided. These results show the evolution of the parameters during the training and the performances of the proposed detectors in terms of error probability and Receiver Operating Characteristics curves. At the end of the study, the obtained results justify the convenience of using IS in the training