Social-pose : Human Trajectory Prediction using Input Pose

Sammanfattning

In this work, we study the benefits of predicting human trajectories using human body poses instead of solely their x-y locations in time. We propose ‘Social-pose’, an attention-based pose encoder that encodes the poses of all humans in the scene and their social relations. Our method can be used as a plugin to any existing trajectory predictor. We explore the advantages to use 2D versus 3D poses, as well as a limited set of poses. We also investigate the attention map to find out which frames of poses are critical to improve human trajectory prediction. We have done extensive experiments on state-of-the-art models (based on LSTMs, GANs and transformers), and showed improvements over all of them on synthetic (Joint Track Auto) and real (Human3.6M and Pedestrians and Cyclists in Road Traffic) datasets.