Transformer Networks for Future Person Localization in First-Person Videos

Reliably and accurately forecasting future trajectories of pedestrians is necessary for systems like autonomous vehicles or visual assistive devices to function correctly. While previous state-of-the-art methods relied on modeling social interactions with LSTMs, with videos captured with a static camera from a bird’s-eye view, our paper presents a new method that leverages the Transformers architecture and offers a reliable way to model future trajectories in first-person videos captured by a body-mounted camera, without having to model any social interactions. Accurately forecasting future trajectories is a challenging task, mainly due to how unpredictably humans move. We tackle this issue by using information about target persons’ previous locations, scales, and dynamic poses, as well as information about the camera wearer’s ego-motion. The model we propose predicts future trajectories in a simple way, modeling each target’s trajectory separately, without the use of complex social interactions between humans or interactions between targets and the scene. Experimental results show that our method overall outperforms previous state-of-the-art methods, and yields better results in challenging situations where previous state-of-the-art methods fail.