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Towards View-Invariant Intersection Recognition from Videos using Deep Network EnsemblesAuthors: Abhijeet Kumar,Gunshi Gupta,Avinash Sharma,Madhava Krishna Conference: 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS-2018 2018) Location Madrid, Spain Date: 2018-10-01 Report no: IIIT/TR/2018/70 AbstractThis paper strives to answer the following question: Is it possible to recognize an intersection when seen from different road segments that constitute the intersection? An intersection or a junction typically is a meeting point of three or four road segments. Its recognition from a road segment that is transverse to or 180 degrees apart from its previous sighting is an extremely challenging and yet a very relevant problem to be addressed from the point of view of both autonomous driving as well as loop detection. This paper formulates this as a problem of video recognition and proposes a novel LSTM based Siamese style deep network for video recognition. For what is indeed a challenging problem and the limited annotated dataset available we show competitive results of recognizing intersections when approached from diverse viewpoints or road segments. Specifically, we tabulate effective recognition accuracy even as the approaches to the intersection being compared are disparate both in terms of viewpoints and weather/illumination conditions. We show competitive results on both synthetic yet highly realistic data mined from the gaming platform GTA as well as on real world data made available through Mapillary. Full paper: pdf Centre for Visual Information Technology |
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