![]() ![]() Differences between cars can be imperceptible to an untrained person for instance, some car models can have subtle changes in tail lights (e.g., 2007 Honda Accord vs. Whereas many challenging tasks in machine vision (such as photo tagging) are easy for humans, the fine-grained object recognition task we perform here is one that few people could accomplish for even a handful of images. We demonstrate that, by deploying a machine vision framework based on deep learning-specifically, Convolutional Neural Networks (CNN)-it is possible to not only recognize vehicles in a complex street scene but also to reliably determine a wide range of vehicle characteristics, including make, model, and year. Our results suggest that automated systems for monitoring demographics may effectively complement labor-intensive approaches, with the potential to measure demographics with fine spatial resolution, in close to real time. For instance, if the number of sedans encountered during a drive through a city is higher than the number of pickup trucks, the city is likely to vote for a Democrat during the next presidential election (88% chance) otherwise, it is likely to vote Republican (82%). (The average US precinct contains ∼1,000 people.) The resulting associations are surprisingly simple and powerful. ![]() Street view ruler tool download code#Data from this census of motor vehicles, which enumerated 22 million automobiles in total (8% of all automobiles in the United States), were used to accurately estimate income, race, education, and voting patterns at the zip code and precinct level. ![]() Using deep learning-based computer vision techniques, we determined the make, model, and year of all motor vehicles encountered in particular neighborhoods. Here, we present a method that estimates socioeconomic characteristics of regions spanning 200 US cities by using 50 million images of street scenes gathered with Google Street View cars. As digital imagery becomes ubiquitous and machine vision techniques improve, automated data analysis may become an increasingly practical supplement to the ACS. Although a comprehensive source of data, the lag between demographic changes and their appearance in the ACS can exceed several years. The United States spends more than $250 million each year on the American Community Survey (ACS), a labor-intensive door-to-door study that measures statistics relating to race, gender, education, occupation, unemployment, and other demographic factors. ![]()
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