Spatial As Deep: Spatial CNN for Traffic Scene Understanding

Understand

Convolutional neural networks (CNNs) are usually built by stacking convolutional operations layer-by-layer.

• Although CNN has shown strong capability to extract semantics from raw pixels, its capacity to capture spatial relationships of pixels across rows and columns of an image is not fully explored.
• These relationships are important to learn semantic objects with strong shape priors but weak appearance coherences, such as traffic lanes, which are often occluded or not even painted on the road surface as shown in Fig.
• 1 (a).