Rapid training data generation from image sequences for pattern recognition

Date

2011-04-15

Author

DİLAN, Rasim Askin
Koku, Ahmet Buğra
Konukseven, Erhan İlhan

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This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

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This study focuses on the development of a novel technique for the rapid generation of artificial neural network training data from video streams. Videos captured on an off-road terrain are used to train artificial neural networks that learn to differentiate road and non-road sections in the captured videos. Contrary to the times-taking frame-by-frame processing, in the proposed method, classification data of road pixels is created concurrently as the video plays. The proposed method is explained in detail and its performance is evaluated against the classical hand-classified image sequences on test videos. The proposed method can also be applied to several other applications using training for recognition.

Subject Keywords

Training Data Generation , QOSS , Artificial Neural Networks , Pattern Recognition , Dataset Down Sampling

URI

https://hdl.handle.net/11511/48768

DOI

https://doi.org/10.1109/icmech.2011.5971329

Collections

Department of Mechanical Engineering, Conference / Seminar

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Citation Formats

R. A. DİLAN, A. B. Koku, and E. İ. Konukseven, “Rapid training data generation from image sequences for pattern recognition,” 2011, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/48768.