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Now that we have defined the entire model, we can start training the neural network. The idea of each step is to take one batch and predict the next position for each of the agents and positions. The result is then compared to the target values through the associated loss function we defined earlier.
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In this blog’s experiments we will utilise the mentioned in previous posts (x,y) coordinate representations as input to the network. Since each of these coordinate representations is associated with a specific agent who will interact with each other, it is important to separate the associated sequences and acknowledge that each prediction will be dependent on the previous sequences observed for a given agent.
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Before considering the details around modelling such tasks, we should spend some time to consider the datasets we will use as well as the preprocessing routines we will consider.
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This tutorial is meant as an introduction to the problem of trajectory generation. It introduces several ways for modelling the motion of agents in pixel space and proposes several ways of preprocessing data. It follows the structure from its associated GitHub repository. Feel free to skip to the end to see the performance of 2 basic models.