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Fast Horizon Estimation via Deep Hashing

In this work, we propose an efficient method for accurately estimating the horizon line in a road scene image. A number of image patches are first cropped from the image and then feed into a convolution neural network which is originally trained for object detection. The yielded deep features from three different layers are compared with the features of the training patches, in a spatial-aware fashion. The horizon line is then estimated via a sophisticated voting stage in which different voters are considered differently according to their importances. The proposed algorithm outperforms the state-of-the-art methods in accuracy and achieves realtime speed (up to 25 fps) as well.