AC-VRNN: Attentive Conditional-VRNN for Multi-Future Trajectory Prediction
This work addresses trajectory prediction for intelligent transportation and robotics, but it is incremental as it builds on existing C-VRNN methods with added attention mechanisms.
The paper tackles multi-future human trajectory prediction in crowded scenes by proposing AC-VRNN, a generative model that uses conditional VRNNs with prior belief maps and graph-based attention for interactions, achieving state-of-the-art results on multiple public datasets.
Anticipating human motion in crowded scenarios is essential for developing intelligent transportation systems, social-aware robots and advanced video surveillance applications. A key component of this task is represented by the inherently multi-modal nature of human paths which makes socially acceptable multiple futures when human interactions are involved. To this end, we propose a generative architecture for multi-future trajectory predictions based on Conditional Variational Recurrent Neural Networks (C-VRNNs). Conditioning mainly relies on prior belief maps, representing most likely moving directions and forcing the model to consider past observed dynamics in generating future positions. Human interactions are modeled with a graph-based attention mechanism enabling an online attentive hidden state refinement of the recurrent estimation. To corroborate our model, we perform extensive experiments on publicly-available datasets (e.g., ETH/UCY, Stanford Drone Dataset, STATS SportVU NBA, Intersection Drone Dataset and TrajNet++) and demonstrate its effectiveness in crowded scenes compared to several state-of-the-art methods.