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Synthetic sequences and ground-truth flow field generation for algorithm validation

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Abstract

Research in computer vision is advancing by the availability of good datasets that help to improve algorithms, validate results and obtain comparative analysis. The datasets can be real or synthetic. For some of the computer vision problems such as optical flow it is not possible to obtain ground-truth optical flow with high accuracy in natural outdoor real scenarios directly by any sensor, although it is possible to obtain ground-truth data of real scenarios in a laboratory setup with limited motion. In this difficult situation computer graphics offers a viable option for creating realistic virtual scenarios. In the current work we present a framework to design virtual scenes and generate sequences as well as ground-truth flow fields. Particularly, we generate a dataset containing sequences of driving scenarios. The sequences in the dataset vary in different speeds of the on-board vision system, different road textures, complex motion of vehicle and independent moving vehicles in the scene. This dataset enables analyzing and adaptation of existing optical flow methods, and leads to invention of new approaches particularly for driver assistance systems.

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Acknowledgments

This work was supported in part by the Spanish Government under Project TIN2011-25606. The work of N. Onkarappa was supported in part by the Catalan Government through the Agency for Management of University and Research Grants (AGAUR) under an FI Grant.

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  1. Computer Vision Center, Campus UAB, 08193, Barcelona, Bellaterra, Spain

    Naveen Onkarappa & Angel D. Sappa

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  1. Naveen Onkarappa
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  2. Angel D. Sappa
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Correspondence to Naveen Onkarappa.

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Onkarappa, N., Sappa, A.D. Synthetic sequences and ground-truth flow field generation for algorithm validation. Multimed Tools Appl 74, 3121–3135 (2015). https://doi.org/10.1007/s11042-013-1771-7

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