Update README.rst

README.rst

@@ -5,7 +5,9 @@ PyTorch implementation of `Human Trajectory Forecasting in Crowds: A Deep Learni
 
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-TrajNet++ is a large scale interaction-centric trajectory forecasting benchmark comprising explicit agent-agent scenarios. Our framework provides proper indexing of trajectories by defining a hierarchy of trajectory categorization. In addition, we provide an extensive evaluation system to test the gathered methods for a fair comparison. In our evaluation, we go beyond the standard distance-based metrics and introduce novel metrics that measure the capability of a model to emulate pedestrian behavior in crowds. Finally, we provide code implementations of > 10 popular human trajectory forecasting baselines.
+TrajNet++ is a large scale interaction-centric trajectory forecasting benchmark comprising explicit agent-agent scenarios. Our framework provides proper indexing of trajectories by defining a hierarchy of trajectory categorization. In addition, we provide an extensive evaluation system to test the gathered methods for a fair comparison. In our evaluation, we go beyond the standard distance-based metrics and introduce novel metrics that measure the capability of a model to emulate pedestrian behavior in crowds. Finally, we provide code implementations of > 15 popular human trajectory forecasting baselines.
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+We host the `Trajnet++ Challenge <https://www.aicrowd.com/challenges/trajnet-a-trajectory-forecasting-challenge>`_ on AICrowd allowing researchers to objectively evaluate and benchmark trajectory forecasting models on interaction-centric data. We rely on the spirit of crowdsourcing and the challenge has > 1800 submissions. We encourage researchers to submit their sequences to TrajNet++, so the quality of trajectory forecasting models can keep increasing in tackling more challenging scenarios.  
 
 
 Data Setup