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.
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Acknowledgements
Fernando M. Quintana would like to acknowledge the Spanish Ministerio de Ciencia, Innovación y Universidades for the support through FPU grant (FPU18/04321). This work was also supported by the project NEMOVISION from the Ministerio de Ciencia e Innovación, PID2019-109465RB-I00/AEI/10.13039/501100011033.
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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. https://doi.org/10.1007/978-3-031-43078-7_52
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