Rapid training data generation from image sequences for pattern recognition

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.