  We observe that the human trajectory is not only forward predictable, but
also backward predictable. Both forward and backward trajectories follow the
same social norms and obey the same physical constraints with the only
difference in their time directions. Based on this unique property, we develop
a new approach, called reciprocal learning, for human trajectory prediction.
Two networks, forward and backward prediction networks, are tightly coupled,
satisfying the reciprocal constraint, which allows them to be jointly learned.
Based on this constraint, we borrow the concept of adversarial attacks of deep
neural networks, which iteratively modifies the input of the network to match
the given or forced network output, and develop a new method for network
prediction, called reciprocal attack for matched prediction. It further
improves the prediction accuracy. Our experimental results on benchmark
datasets demonstrate that our new method outperforms the state-of-the-art
methods for human trajectory prediction.
