A Parametric Top-View Representation of Complex Road Scenes

Publication Date: 6/16/2019

Event: IEEE Computer Vision and Pattern Recognition (CVPR 2019)

Reference: pp. 10325-10333

Authors: Ziyan Wang , Carnegie Mellon University, NEC Laboratories America, Inc.; Buyu Liu, NEC Laboratories America, Inc.; Samuel Schulter, NEC Laboratories America, Inc.; Manmohan Chandraker, NEC Laboratories America, Inc.

Abstract: In this paper, we address the problem of inferring the layout of complex road scenes given a single camera as input. To achieve that, we first propose a novel parameterized model of road layouts in a top-view representation, which is not only intuitive for human visualization but also provides an interpretable interface for higher-level decision making. Moreover, the design of our top-view scene model allows for efficient sampling and thus generation of large-scale simulated data, which we leverage to train a deep neural network to infer our scene model’s parameters. Specifically, our proposed training procedure uses supervised domain-adaptation techniques to incorporate both simulated as well as manually annotated data. Finally, we design a Conditional Random Field (CRF) that enforces coherent predictions for a single frame and encourages temporal smoothness among video frames. Experiments on two public data sets show that: (1) Our parametric top-view model is representative enough to describe complex road scenes; (2) The proposed method outperforms baselines trained on manually-annotated or simulated data only, thus getting the best of both; (3) Our CRF is able to generate temporally smoothed while semantically meaningful results.

Publication Link: https://openaccess.thecvf.com/content_CVPR_2019/html/Wang_A_Parametric_Top-View_Representation_of_Complex_Road_Scenes_CVPR_2019_paper.html

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