Vehicle point of interest detection using in-car data

Intelligent transportation systems often identify and make use of locations extracted from GPS trajectories to make informed decisions. However, many of the locations identified by existing systems are false positives, such as those in heavy traffic. Signals from the vehicle, such as speed and seatbelt status, can be used to identify these false positives. In this paper, we (i) demonstrate the utility of the Gradient-based Visit Extractor (GVE) in the automotive domain, (ii) propose a classification stage for removing false positives from the location extraction process, and (iii) evaluate the effectiveness of these techniques in a high resolution vehicular dataset

Using Deep Learning and Google Street View to Estimate the Demographic Makeup of the US

The United States spends more than $1B each year on initiatives such as 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. Although a comprehensive source of data, the lag between demographic changes and their appearance in the ACS can exceed half a decade. As digital imagery becomes ubiquitous and machine vision techniques improve, automated data analysis may provide a cheaper and faster alternative. Here, we present a method that determines socioeconomic trends from 50 million images of street scenes, gathered in 200 American cities by Google Street View cars. Using deep learning-based computer vision techniques, we determined the make, model, and year of all motor vehicles encountered in particular neighborhoods. Data from this census of motor vehicles, which enumerated 22M automobiles in total (8% of all automobiles in the US), was used to accurately estimate income, race, education, and voting patterns, with single-precinct resolution. (The average US precinct contains approximately 1000 people.) The resulting associations are surprisingly simple and powerful. For instance, if the number of sedans encountered during a 15-minute 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%). Our results suggest that automated systems for monitoring demographic trends may effectively complement labor-intensive approaches, with the potential to detect trends with fine spatial resolution, in close to real time.Comment: 41 pages including supplementary material. Under review at PNA