Exploiting Event-Driven Cameras for Spatio-Temporal Prediction of Fast-Changing Trajectories
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This paper investigates solutions to trajectory prediction problems for artificial intelligence in robotics, to improve moving target interception, such as catching a bouncing ball. Unexpected, highly-non-linear trajectories cannot easily be solved with regression-based prediction, and as such, we look to learning methods. In addition, fast-moving targets are better sensed using recent event cameras, which produce an asynchronous output triggered by spatial change, rather than a fixed time period as with traditional cameras. We investigate how LSTM models can be adapted for event camera data, and in particular look at the benefit of using asynchronous data compared to synchronous sampling methods.
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