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Abstract:

Proliferating autonomous devices necessitate prediction of pedestrian trajectories in order to navigate realistic environments. Such prediction is currently done using a non-holistic approach based on pedestrian coordinates, with additional intertwined information from scene images. This paper presents a holistic approach that generates image predictions based on image inputs to facilitate more efficient decision making in autonomous devices. This is made possible by manipulating the common architecture from a GAN with LSTM encoder and decoder to a GAN where the LSTM sits in the low dimensionality space of the GAN.