Understanding cities with machine eyes: A review of deep computer vision in urban analytics

Modelling urban systems has interested planners and modellers for decades. Different models have been achieved relying on mathematics, cellular automation, complexity, and scaling. While most of these models tend to be a simplification of reality, today within the paradigm shifts of artificial intelligence across the different fields of science, the applications of computer vision show promising potential in understanding the realistic dynamics of cities. While cities are complex by nature, computer vision shows progress in tackling a variety of complex physical and non-physical visual tasks. In this article, we review the tasks and algorithms of computer vision and their applications in understanding cities. We attempt to subdivide computer vision algorithms into tasks, and cities into layers to show evidence of where computer vision is intensively applied and where further research is needed. We focus on highlighting the potential role of computer vision in understanding urban systems related to the built environment, natural environment, human interaction, transportation, and infrastructure. After showing the diversity of computer vision algorithms and applications, the challenges that remain in understanding the integration between these different layers of cities and their interactions with one another relying on deep learning and computer vision. We also show recommendations for practice and policy-making towards reaching AI-generated urban policies

VisionBlocks: A Social Computer Vision Framework

Vision Blocks (http://visionblocks.org) is an on demand, in-browser, customizable computer vision application publishing platform for masses. It empowers end-users (consumers)to create novel solutions for themselves that they would not easily obtain off-the-shelf. By transferring design capability to the consumers, we enable creation and dissemination of custom products and algorithms. We adapt a visual programming paradigm to codify vision algorithms for general use. As a proof of-concept, we implement computer vision algorithms such as motion tracking, face detection, change detection and others. We demonstrate their applications on real-time video. Our studies show that end users (non programmers) only need 50% more time to build such systems when compared to the most experienced researchers. We made progress towards closing the gap between researchers and consumers by finding that users rate the intuitiveness of the approach in a level 6% less than researchers. We discuss different application scenarios where such study will be useful and argue its benefit for computer vision research community. We believe that enabling users with ability to create application will be first step towards creating social computer vision applications and platform.Alfred P. Sloan Foundation (Research Fellowship

Artificial Vision Algorithms for Socially Assistive Robot Applications: A Review of the Literature

Today, computer vision algorithms are very important for different fields and applications, such as closed-circuit television security, health status monitoring, and recognizing a specific person or object and robotics. Regarding this topic, the present paper deals with a recent review of the literature on computer vision algorithms (recognition and tracking of faces, bodies, and objects) oriented towards socially assistive robot applications. The performance, frames per second (FPS) processing speed, and hardware implemented to run the algorithms are highlighted by comparing the available solutions. Moreover, this paper provides general information for researchers interested in knowing which vision algorithms are available, enabling them to select the one that is most suitable to include in their robotic system applicationsBeca Conacyt Doctorado No de CVU: 64683