SpikeBALL: Neuromorphic Dataset for Object Tracking

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URI: http://hdl.handle.net/10498/33156

DOI: 10.1007/978-3-031-43078-7_52

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Author/s
Guerrero Vázquez, ElisaAuthority UCA; Quintana Velázquez, Fernando ManuelAuthority UCA; Guerrero Lebrero, María de la PazAuthority UCA
Date
2023
Department
Ingeniería Informática
Source
Guerrero-Lebrero, M.P., Quintana, F.M., Guerrero, E. (2023). SpikeBALL: Neuromorphic Dataset for Object Tracking. In: Rojas, I., Joya, G., Catala, A. (eds) Advances in Computational Intelligence. IWANN 2023. Lecture Notes in Computer Science, vol 14135. Springer, Cham.
Abstract
Most of widely used datasets are not suitable for Spiking Neural Networks (SNNs) due to the need to encode the static data into spike trains and then put them into the network. In addition, the majority of these datasets have been generated to classify objects and can not be used to solve object tracking problems. Therefore, we propose a new neuromorphic dataset, SpikeBALL, for object tracking that contributes to improve the development of the SNN algorithm for these type of problems.
Subjects
spiking neural networks; event cameras; object tracking,; event-based dataset
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Attribution-NonCommercial-NoDerivatives 4.0 Internacional
This work is under a Creative Commons License Attribution-NonCommercial-NoDerivatives 4.0 Internacional