Three-dimensional reconstruction using SFM for actual pedestrian classification

In recent years, the popularity of intelligent and autonomous vehicles has grown notably. In fact, there already exist commercial models with a high degree of autonomy as regards self-driving capabilities. A key feature for this kind of vehicle is object detection, which is commonly performed in 2D space. This has some inherent issues as an object and the depiction of such an object would be classified as the actual object, which is inadequate since urban environments are full of billboards, printed adverts and posters that would likely make these systems fail. In order to overcome this problem, a 3D sensor could be leveraged, although this would make the platform more expensive, energy inefficient and computationally complex. Thus, we propose the use of structure from motion to reconstruct the three-dimensional information of the scene from a set of images, and merge the 2D and 3D data to differentiate actual objects from depictions. As expected, our approach is able to work with a regular color camera. No 3D sensors whatsoever are required. As the experiments confirm, our approach is able to distinguish between actual pedestrians and depictions of them more than 87% of times in synthetic and real-world tests in the worst scenarios, while the accuracy is of almost 98% in the best case. • A method to robustly tell apart actual objects from depictions in urban environments. • The method uses deep learning algorithms with pattern matching and analytical methods. • Exhaustive experimentation on synthetic, real data and a state of the art dataset.