Conditioned Human Trajectory Prediction using Iterative Attention Blocks
This addresses motion prediction for robotics and surveillance, but it is incremental as it matches existing performance without new major gains.
The paper tackles pedestrian trajectory prediction in urban environments by conditioning on maps and surrounding agents, achieving results comparable to state-of-the-art models using metrics like ADE and FDE.
Human motion prediction is key to understand social environments, with direct applications in robotics, surveillance, etc. We present a simple yet effective pedestrian trajectory prediction model aimed at pedestrians positions prediction in urban-like environments conditioned by the environment: map and surround agents. Our model is a neural-based architecture that can run several layers of attention blocks and transformers in an iterative sequential fashion, allowing to capture the important features in the environment that improve prediction. We show that without explicit introduction of social masks, dynamical models, social pooling layers, or complicated graph-like structures, it is possible to produce on par results with SoTA models, which makes our approach easily extendable and configurable, depending on the data available. We report results performing similarly with SoTA models on publicly available and extensible-used datasets with unimodal prediction metrics ADE and FDE.