Domain Adaptation for Traffic Density Estimation

Convolutional Neural Networks have produced state-of-the-art results for a multitude of computer vision tasks under supervised learning. However, the crux of these methods is the need for a massive amount of labeled data to guarantee that they generalize well to diverse testing scenarios. In many real-world applications, there is indeed a large domain shift between the distributions of the train (source) and test (target) domains, leading to a significant drop in performance at inference time. Unsupervised Domain Adaptation (UDA) is a class of techniques that aims to mitigate this drawback without the need for labeled data in the target domain. This makes it particularly useful for the tasks in which acquiring new labeled data is very expensive, such as for semantic and instance segmentation. In this work, we propose an end-to-end CNN-based UDA algorithm for traffic density estimation and counting, based on adversarial learning in the output space. The density estimation is one of those tasks requiring per-pixel annotated labels and, therefore, needs a lot of human effort. We conduct experiments considering different types of domain shifts, and we make publicly available two new datasets for the vehicle counting task that were also used for our tests. One of them, the Grand Traffic Auto dataset, is a synthetic collection of images, obtained using the graphical engine of the Grand Theft Auto video game, automatically annotated with precise per-pixel labels. Experiments show a significant improvement using our UDA algorithm compared to the model's performance without domain adaptation. The code, the models and the datasets are freely available at https://ciampluca.github.io/unsupervised counting.