SpikeBALL: Neuromorphic Dataset for Object Tracking

Guerrero Vázquez, Elisa; Quintana Velázquez, Fernando Manuel; Guerrero Lebrero, María de la Paz

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

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Autor/es

Guerrero Vázquez, Elisa

; Quintana Velázquez, Fernando Manuel

; Guerrero Lebrero, María de la Paz

Fecha

2023

Departamento/s

Ingeniería Informática

Fuente

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.

Resumen

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.

Materias

spiking neural networks; event cameras; object tracking,; event-based dataset

Colecciones

Esta obra está bajo una Licencia Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 Internacional

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