When Do Luxury Cars Hit the Road? Findings by A Big Data Approach
This study addresses the need for scalable and diverse data collection for marketing insights into car owners, though it is incremental as it applies existing methods to a new dataset.
The paper tackled the problem of inferring car owners' lifestyles by analyzing when different car makes appear on roads, using a fully automatic method based on surveillance camera data and achieving results from 50,000 images processed with faster R-CNN and VGG16 models.
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.