Abstract
We report a strict 3D pyramidal neural network model based on convolutional neural networks and the concept of pyramidal images for person re-identification in video surveillance. Main advantage of the model is that it also maintains the spatial topology of the input image, while presenting a simple connection scheme with lower computational and memory costs than in other neural networks. Challenging results are reported for person re-identification in real-world environments.
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Iodice, S., Petrosino, A., Ullah, I. (2016). Strict Pyramidal Deep Architectures for Person Re-identification. In: Bassis, S., Esposito, A., Morabito, F., Pasero, E. (eds) Advances in Neural Networks. WIRN 2015. Smart Innovation, Systems and Technologies, vol 54. Springer, Cham. https://doi.org/10.1007/978-3-319-33747-0_18
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DOI: https://doi.org/10.1007/978-3-319-33747-0_18
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