A combined recognition and segmentation model for urban traffic scene understanding

The perception of traffic related objects in the vehicles environment is an essential prerequisite for future autonomous driving. Cameras are particularly suited for this task, as the traffic relevant information of a scene is inferable from its visual appearance. In traffic scene understanding, semantic segmentation denotes the task of generating and labeling regions in the image that correspond to specific object categories, such as cars or road area. In contrast, the task of scene recognition assigns a global label to an image, that reflects the overall category of the scene. This paper presents a deep neural network (DNN) capable of solving both problems in a computationally efficient manner. The architecture is designed to avoid redundant computations, as the task specific decoders share a common feature encoder stage. A novel Hadamard layer with element-wise weights efficiently exploits spatial priors for the segmentation task. Traffic scene segmentation is investigated in conjunction with road topology recognition based on the cityscapes dataset [1] augmented with manually labeled road topology ground truth data.