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While the literature has been fairly dense in the areas of sceneunderstanding and semantic labeling there have been few works that make use ofmotion cues to embellish semantic performance and vice versa. In this paper, weaddress the problem of semantic motion segmentation, and show how semantic andmotion priors augments performance. We pro- pose an algorithm that jointlyinfers the semantic class and motion labels of an object. Integrating semantic,geometric and optical ow based constraints into a dense CRF-model we infer boththe object class as well as motion class, for each pixel. We found improvementin performance using a fully connected CRF as compared to a standardclique-based CRFs. For inference, we use a Mean Field approximation basedalgorithm. Our method outperforms recently pro- posed motion detectionalgorithms and also improves the semantic labeling compared to thestate-of-the-art Automatic Labeling Environment algorithm on the challengingKITTI dataset especially for object classes such as pedestrians and cars thatare critical to an outdoor robotic navigation scenario.

Semantic Motion Segmentation Using Dense CRF Formulation