When Do Luxury Cars Hit the Road? Findings by A Big Data Approach

TL;DR

This study uses high-resolution surveillance images and deep learning to analyze the appearance times of different luxury cars on the road, revealing insights into owner lifestyles and aiding targeted marketing.

Contribution

It introduces an automated, large-scale method using deep learning on publicly available camera data to study car appearance times, surpassing traditional questionnaire-based approaches.

Findings

Luxury cars tend to appear at specific times indicating owner lifestyles.

Deep learning models effectively identify car makes from surveillance images.

The approach enables scalable and diverse data collection for automotive lifestyle analysis.

Abstract

In this paper, we focus on studying the appearing time of different kinds of cars on the road. This information will enable us to infer the life style of the car owners. The results can further be used to guide marketing towards car owners. Conventionally, this kind of study is carried out by sending out questionnaires, which is limited in scale and diversity. To solve this problem, we propose a fully automatic method to carry out this study. Our study is based on publicly available surveillance camera data. To make the results reliable, we only use the high resolution cameras (i.e. resolution greater than $1280 \times 720$). Images from the public cameras are downloaded every minute. After obtaining 50,000 images, we apply faster R-CNN (region-based convoluntional neural network) to detect the cars in the downloaded images and a fine-tuned VGG16 model is used to recognize the car makes. Based on the recognition results, we present a data-driven analysis on the relationship between car makes and their appearing times, with implications on lifestyles.