Evaluation of Synthetic Video Data in Machine Learning Approaches for Parking Space Classification

Most modern computer vision techniques rely on large amounts of meticulously annotated data for training and evaluation. In close-to-market development, this demand is even higher since numerous common and—more important—less common situations have to be tested and must hence be covered datawise. However, gathering the necessary amount of data ready-labeled for the task at hand is a challenge of its own. Depending on the complexity of the objective and the chosen approach, the required amount of data can be vast. At the same time, the effort to capture all possible cases of a given problem grows with their variability. This makes recording new video data unfeasible, even impossible at times. In this work, we regard parking space classification as an exemplary application to target the imbalance of cost and benefit w.r.t. image data creation for machine learning approaches. We rely on a fully-fledged park deck simulation created with Unreal Engine 4 for data creation and replace all conventionally recorded and hand-labeled training data by automatically-annotated synthetic video data. We train several of-the-shelf classifiers with a common choice of feature inputs on synthetic images only and evaluate them on two realworld sequences of different outdoor car parks. We reach a classification performance that matches our previous work on this task in which all classifiers were developed solely with real-life video data.