A new approach for prediction of pedestrian trajectories based only on images.
Thursday, May 09, 2019, 10:30am
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.
Location : CoRE A 301
Prof. Kristin Dana, Prof. Casimir Kulikowski, Prof. Kostas Bekris and Prof. Desheng Zhang
Event Type: Qualifying Exam