engleski

Lightweight convolutional models for real-time dense prediction and forecasting

Recent advances in deep convolutional models have caused unprecedented growth of computer vision performance. This has opened exciting applications in the fields of smart vehicles and safe roads. Pixel-level image understanding can be achieved by associating each image window with a meaningful class such as ‘road’, ‘terrain’, ‘sidewalk’ or ‘person’. The resulting semantic map reveals the kind of surface terrain in front of the vehicle, and may be used to recover the traversability map required for motion planning. Depth can be recovered by predicting a disparity field which maximizes similarity between two stereo images.

computer vision

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