Body orientation estimation through graph representation: Expanding accuracy with Data Augmentation and Gradient Boosting

Body Orientation Estimation (BOE) is important for a wide array of applications, including robotics, surveillance and consumer analysis. Although multi-sensor approaches are effective, they are not a viable option for in the wild scenarios; the usual approach in such cases is to use single camera images, with imprecise results. Some applications that deal with people benefit from obtaining 2D human skeletons for gesture recognition, and these skeletons bring valuable information about the person's pose. It is proposed to build a 2D skeleton via OpenPose and using its data as training data on XGBoost to detect BOE. To evaluate predictions considering real situations based on a single camera, the TUD Multiview Pedestrian dataset is used and extended considering that a single person is originally considered in images where more people were often identified. It is compared the proposed approach against various state-of-the-art methods and our results indicate better performance. Finally, it is proved that our method is viable for BOE in real-time scenarios by presenting case studies on simulated scenes.