Abstract
We consider the problem of processing point cloud sequences. In particular, we represent and track objects in dynamic scenes acquired using low-cost 3D sensors such as the Kinect. A neural network based approach is proposed to represent and estimate 3D objects motion. This system addresses multiple computer vision tasks such as object segmentation, representation, motion analysis and tracking. The use of a neural network allows the unsupervised estimation of motion and the representation of objects in the scene. This proposal avoids the problem of finding corresponding features while tracking moving objects. A set of experiments are presented that demonstrate the validity of our method to track 3D objects. Favorable results are presented demonstrating the capabilities of the GNG algorithm for this task.
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Orts-Escolano, S., Garcia-Rodriguez, J., Morell, V., Cazorla, M., Garcia-Garcia, A., Ovidiu-Oprea, S. (2015). Optimized Representation of 3D Sequences Using Neural Networks. In: Ferrández Vicente, J., Álvarez-Sánchez, J., de la Paz López, F., Toledo-Moreo, F., Adeli, H. (eds) Bioinspired Computation in Artificial Systems. IWINAC 2015. Lecture Notes in Computer Science(), vol 9108. Springer, Cham. https://doi.org/10.1007/978-3-319-18833-1_27
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DOI: https://doi.org/10.1007/978-3-319-18833-1_27
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-18832-4
Online ISBN: 978-3-319-18833-1
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