Image, Video and Volume Datasets

Mobile Apps User Interfaces

Intelligent Vehicle


This project aims to develop models for vehicular perception and control through passive video and inertial sensor signals acquisition and newest Artificial Intelligence. Vehicular perception comprises exteroception and proprioception. Exteroception aims to understand the environment outside the vehicle, recognizing the path features on which it travels. These features include transient events in the form of anomalies and obstacles, such as potholes, cracks, speed bumps, etc.; and persistent events, such as surface type, conservation condition and the road surface quality. On the other hand, proprioception aims to understand vehicular movements in order to allow the vehicle identify its own behavior and attitude. The identifications can also be transient in the form of driving events, such as lane change, braking, skidding, aquaplaning, turning right or left; and persistent, as a safe or dangerous driving behavior profile.


For the development of this project, we are collecting datasets in Brazilian locations, more specifically upstate  Santa Catarina, with contextual variations using GPS, camera and inertial sensors, represented by accelerometers and gyroscopes. These data are being produced in many different vehicles, driven by different drivers, traveling through different environments, from highways down to badly maintained dirt roads. To recognize and classify the vehicular perception patterns, we have developed several models based on Artificial Intelligence and Deep Learning. Below we describe the datasets produced, models developed and the results obtained, together with published article and source-codes.

The project is active and we are currently developing new models for new perception patterns recognition. Below are described the research progress. Published articles can also be found on our project page at Projeto Veículo Autônomo and also at Research Gate.



Precision Agriculture/UAV Images


Meteorology and Solar Energy/Sky Imager Datasets





Sobre o Autor

possui graduação em Ciências da Computação pela Universidade Federal de Santa Catarina (1989) e Doutorado Acadêmico (Dr. rer.nat.) em Ciências da Computação pela Universidade de Kaiserslautern (1996). Atualmente é professor Titular da Universidade Federal de Santa Catarina, onde é professor do Programa de Pós-graduação em Ciência da Computação e dos cursos de graduação em Ciências da Computação e Sistemas de Informação. Tem experiência nas áreas de Informática em Saúde, Processamento e Análise de Imagens e Engenharia Biomédica, com ênfase em Telemedicina, Telerradiologia, Sistemas de Auxílio ao Diagnóstico por Imagem e Processamento de Imagens Médicas, com foco nos seguintes temas: analise inteligente de imagens, DICOM, CBIR, informática médica, visão computacional e PACS. Coordena o Instituto Nacional de Ciência e Tecnologia para Convergência Digital - INCoD. Foi o criador e primeiro Coordenador do Núcleo de Telessaúde de Santa Catarina no âmbito do Programa Telessaúde Brasil do Ministério da Saúde e da OPAS - Organização Pan-Americana de Saúde e criador do Núcleo Santa Catarina da RUTE - Rede Universitária de Telemedicina.