Interpretable Social Anchors for Human Trajectory Forecasting in Crowds

• URL: http://arxiv.org/abs/2105.03136v1
• Date: Fri, 7 May 2021 09:22:34 GMT
• Title: Interpretable Social Anchors for Human Trajectory Forecasting in Crowds
• Authors: Parth Kothari, Brian Sifringer and Alexandre Alahi
• Abstract summary: We propose a neural network-based system to predict human trajectory in crowds. We learn interpretable rule-based intents, and then utilise the expressibility of neural networks to model scene-specific residual. Our architecture is tested on the interaction-centric benchmark TrajNet++.
• Score: 84.20437268671733
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Human trajectory forecasting in crowds, at its core, is a sequence prediction problem with specific challenges of capturing inter-sequence dependencies (social interactions) and consequently predicting socially-compliant multimodal distributions. In recent years, neural network-based methods have been shown to outperform hand-crafted methods on distance-based metrics. However, these data-driven methods still suffer from one crucial limitation: lack of interpretability. To overcome this limitation, we leverage the power of discrete choice models to learn interpretable rule-based intents, and subsequently utilise the expressibility of neural networks to model scene-specific residual. Extensive experimentation on the interaction-centric benchmark TrajNet++ demonstrates the effectiveness of our proposed architecture to explain its predictions without compromising the accuracy.

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