Toward a large-scale multimodal event-based dataset for neuromorphic deep learning applications

Sarah Leung; E. Jared Shamwell; Christopher Maxey; William D. Nothwang

doi:10.1117/12.2305504

14 May 2018 Toward a large-scale multimodal event-based dataset for neuromorphic deep learning applications

Sarah Leung, E. Jared Shamwell, Christopher Maxey, William D. Nothwang

Proceedings Volume 10639, Micro- and Nanotechnology Sensors, Systems, and Applications X; 106391T (2018) https://doi.org/10.1117/12.2305504
Event: SPIE Defense + Security, 2018, Orlando, FL, United States

Abstract

We discuss our efforts with event-based vision and describe our large-scale, heterogeneous robotic dataset that will add to the growing number of event-based datasets currently publicly available. Our dataset comprises over 10 hours of runtime from a mobile robot equipped with two DAVIS240C cameras and an Astra depth camera randomly wandering in an indoor environment while two other independently moving robots randomly wander in the same scene. Vicon ground truth pose is provided for all three robots. To our knowledge, this is the largest event-based dataset with ground truthed independently moving entities.

Conference Presentation

Citation Download Citation

Sarah Leung, E. Jared Shamwell, Christopher Maxey, and William D. Nothwang "Toward a large-scale multimodal event-based dataset for neuromorphic deep learning applications", Proc. SPIE 10639, Micro- and Nanotechnology Sensors, Systems, and Applications X, 106391T (14 May 2018); https://doi.org/10.1117/12.2305504

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