One of the most relevant tasks in an intelligent vehicle navigation system is the detection of obstacles. It is important that a visual perception system for navigation purposes identifies obstacles, ...and it is also important that this system can extract essential information that may influence the vehicle’s behavior, whether it will be generating an alert for a human driver or guide an autonomous vehicle in order to be able to make its driving decisions. In this paper we present an approach for the identification of obstacles and extraction of class, position, depth and motion information from these objects that employs data gained exclusively from passive vision. We use a convolutional neural network for the obstacles detection, optical flow for the analysis of movement of the detected obstacles, both in relation to the direction and in relation to the intensity of the movement, and also stereo vision for the analysis of distance of obstacles in relation to the vehicle. We performed our experiments on two different datasets, and the results obtained showed a good efficacy from the use of depth and motion patterns to assess the obstacles’ potential threat status.
We present a literature review to analyze the state of the art in the area of road detection based upon frontal images. For this purpose, a systematic literature review (SLR) was conducted that ...focuses on analyzing region-based works, since they can adapt to different surface types and do not depend on road geometry or lane markings. Through the comprehensive study of publications in a 11-year time frame, we analyze the methods that are being used, on which types of surface they are applied, whether they are adaptive in relation to surface changes, and whether they are able to distinguish possible faults or changes in the road, such as potholes, shadows, and puddles.
Obstacle detection is one of the main tasks in intelligent vehicle navigation systems. Several research works focused on the use of passive vision (cameras) to accomplish this task have been ...published. In this paper we present a literature mapping of the state of the art in road obstacle detection using frontal images. This mapping is based upon a systematic literature review that took into consideration papers published between 2007 and 2019. We analyze approaches based upon methods such as image segmentation, stereo vision, optical flow and neural networks and classify them accordingly to their characteristics and detection targets, such as vehicles, pedestrians or obstacles in general. We also inspect if they are performing pavement defects detection, such as potholes, puddles or other types of damage. The detection of pavement problems is important for the reality of in-development countries, which in many cases do not present well-maintained roads and may represent a threat to vehicular navigation. With this mapping we can identify the current state in this research area and also discuss future steps.
The current work describes the use of multidimensional Euclidean geometric distance (EGD) and Bayesian methods to characterize and classify the sky and cloud patterns present in image pixels. From ...specific images and using visualization tools, it was noticed that sky and cloud patterns occupy a typical locus on the red-green-blue (RGB) color space. These two patterns were linearly distributed parallel to the RGB cube's main diagonal at distinct distances. A characterization of the cloud and sky patterns EGD was done by supervision to eliminate errors due to outlier patterns in the analysis. The exploratory data analysis of EGD for sky and cloud patterns showed a Gaussian distribution, allowing generalizations based on the central limit theorem. An intensity scale of brightness is proposed from the Euclidean geometric projection (EGP) on the RGB cube's main diagonal. An EGD-based classification method was adapted to be properly compared with existing ones found in related literature, because they restrict the examined color-space domain. Elimination of this limitation was considered a sufficient criterion for a classification system that has resource restrictions. The EGD-adapted results showed a correlation of 97.9% for clouds and 98.4% for sky when compared to established classification methods. It was also observed that EGD was able to classify cloud and sky patterns invariant to their brightness attributes and with reduced variability because of the sun zenith angle changes. In addition, it was observed that Mie scattering could be noticed and eliminated (together with the reflector's dust) as an outlier during the analysis. Although Mie scattering could be classified with additional analysis, this is left as a suggestion for future work.
The teaching of sorting algorithms is an essential topic in undergraduate computing courses. Typically the courses are taught through traditional lectures and exercises involving the implementation ...of the algorithms. As an alternative, this article presents the design and evaluation of three educational games for teaching Quicksort and Heapsort. The games have been evaluated in a series of case studies, including 23 applications of the games in data structures courses at the Federal University of Santa Catarina with the participation of a total of 371 students. The results provide a first indication that such educational games can contribute positively to the learning outcome on teaching sorting algorithms, supporting the students to achieve learning on higher levels as well as to increase the students' motivation on this topic. The social interaction the games promote allows the students to cooperate or compete while playing, making learning more fun.
BACKGROUNDBrugada Syndrome is an inherited arrhythmogenic disorder characterized by the presence of specific electrocardiographic features with or without clinical symptoms. The patients present ...increased risk of sudden death due to ventricular fibrillation. The prevalence of this electrocardiographic pattern differs according to the studied region. However, epidemiological information including the Brazilian population is scarce. OBJECTIVESTo assess the prevalence of the electrocardiographic pattern of Brugada syndrome and the epidemiological profile associated with it. METHODSCross-sectional study that included 846,533 ECG records of 716,973 patients from the electrocardiogram (ECG) database from the Santa Catarina Telemedicine Network over a 4-year period. All tests were 12-lead conventional ECG (without V1 and V2 in high positions). The tests revealing "Brugada Syndrome" diagnosis (Types 1 and 2) were reviewed by a cardiac electrophysiologist. The level of significance was set at p<0.05. RESULTSIn total, 83 patients had a pattern potentially consistent with Brugada-type pattern ECG. Of these, 33 were confirmed having Brugada-type 1, and 22 with type 2 ECG after reevaluation. The prevalence of Brugada-type 1 ECG was 4.6 per 100,000 patients. Brugada-type 1 ECG was associated with the male gender (81.8% vs. 41.5%, p<0.001) and a lower prevalence of obesity diagnosis (9.1% vs. 26.4%, p=0.028). CONCLUSIONSThis study showed low prevalence of Brugada-type ECG in Southern Brazil. The presence of Brugada-type 1 ECG was associated with the male gender and lower prevalence of obesity diagnosis comparing to the general population.
The demand for several sources of situational data from the traffic environment has intensified in recent years, through the development of applications in intelligent transport systems (ITS), such ...as autonomous vehicles and advanced driver assistance systems. Among these situational data, the road surface type classification is one of the most important and can be used throughout the ITS domain. However, in order to have a wide application, the development of a safe and reliable model is necessary. Therefore, in addition to the application of safe technology, the model developed must operate correctly in different vehicles, with different driving styles and in different environments in which vehicles can travel to. For this purpose, in this work we collect nine datasets with contextual variations using inertial sensors, represented by accelerometers and gyroscopes. These data were produced in three different vehicles, with three different drivers, in three different environments in which there are three different surface types, in addition to variations in conservation state and presence of obstacles and anomalies, such as speed bumps and potholes. After a pre-processing step, these data were used in 34 different computational models for road surface type classification, employing both Classical Machine Learning and Deep Learning techniques. Through several experiments, we analyze the learning and generalization capacity of each technique. The best model developed was a CNN-based deep neural network, which obtained validation accuracy of 93.17%, classifying surfaces between segments of dirt, cobblestone or asphalt roads.
In this paper we present a comparison of supervised classifiers and image features for crop row segmentation of aerial images captured from an unmanned aerial vehicle (UAV). The main goal is to ...investigate which methods are the most suitable to solve this specific problem, as well as to test quantitatively how well they perform for robust segmentation of row patterns. For this purpose, we conducted a systematic literature review over the recent methods specifically designed for aerial image crop row segmentation, and for comparison purposes we implemented the most prominent approaches. Most used Color-texture features were faced against most used classifiers, resulting into a total of 48 combinations, usually having their construction concepts based on the following two step-procedures: (i) supervised training step to build some model over the selected color-texture feature space which is also based upon user-selected samples from the input image; and (ii) classification step, where each pixel of the input image is classified employing the corresponding classifier. The obtained results were compared against a Ground-Truth (GT) image, performed by a human expert, using two distinct evaluation metrics, indicating the most suitable combination of color-texture descriptors and classifiers able to solve the segmentation problem of specific cultures obtained from UAV images.
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