Geraldo P. Rocha Filho

Ana Rosalía Huamán Reyna, Alex Josué Flórez Farfán, Geraldo Pereira Rocha Filho et al.

Currently, there are computer vision systems that help us with tasks that would be dull for humans, such as surveillance and vehicle tracking. An important part of this analysis is to identify traffic anomalies. An anomaly tells us that something unusual has happened, in this case on the highway. This paper aims to model vehicle tracking using computer vision to detect traffic anomalies on a highway. We develop the steps of detection, tracking, and analysis of traffic: the detection of vehicles from video of urban traffic, the tracking of vehicles using a bipartite graph and the Convex Hull algorithm to delimit moving areas. Finally for anomaly detection we use two data structures to detect the beginning and end of the anomaly. The first is the QuadTree that groups vehicles that are stopped for a long time on the road and the second that approaches vehicles that are occluded. Experimental results show that our method is acceptable on the Track4 test set, with an F1 score of 85.7% and a mean squared error of 25.432.

Li Weigang, Liriam Enamoto, Denise Leyi Li et al.

This article reviews the "Once learning" mechanism that was proposed 23 years ago and the subsequent successes of "One-shot learning" in image classification and "You Only Look Once - YOLO" in objective detection. Analyzing the current development of Artificial Intelligence (AI), the proposal is that AI should be clearly divided into the following categories: Artificial Human Intelligence (AHI), Artificial Machine Intelligence (AMI), and Artificial Biological Intelligence (ABI), which will also be the main directions of theory and application development for AI. As a watershed for the branches of AI, some classification standards and methods are discussed: 1) Human-oriented, machine-oriented, and biological-oriented AI R&D; 2) Information input processed by Dimensionality-up or Dimensionality-reduction; 3) The use of one/few or large samples for knowledge learning.

Diego O. Rodrigues, Frances A. Santos, Geraldo P. Rocha Filho et al.

The growing of cities has resulted in innumerable technical and managerial challenges for public administrators such as energy consumption, pollution, urban mobility and even supervision of private and public spaces in an appropriate way. Urban Computing emerges as a promising paradigm to solve such challenges, through the extraction of knowledge, from a large amount of heterogeneous data existing in urban space. Moreover, Urban Computing correlates urban sensing, data management, and analysis to provide services that have the potential to improve the quality of life of the citizens of large urban centers. Consider this context, this chapter aims to present the fundamentals of Urban Computing and the steps necessary to develop an application in this area. To achieve this goal, the following questions will be investigated, namely: (i) What are the main research problems of Urban Computing?; (ii) What are the technological challenges for the implementation of services in Urban Computing?; (iii) What are the main methodologies used for the development of services in Urban Computing?; and (iv) What are the representative applications in this field?