Have i reached the intersection: A deep learning-based approach for intersection detection from monocular cameras

Bhatt, Dhaivat and Sodhi, Danish and Pal, Arghya and Balasubramanian, Vineeth N and Krishna, Madhava (2017) Have i reached the intersection: A deep learning-based approach for intersection detection from monocular cameras. In: IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 24-28 September 2017, Vancouver, BC, Canada.

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Abstract

Long-short term memory networks(LSTM) models have shown considerable performance on variety of problems dealing with sequential data. In this paper, we propose a variant of Long-Term Recurrent Convolutional Network(LRCN) to detect road intersection. We call this network as IntersectNet. We pose road intersection detection as binary classification task over sequence of frames. The model combines deep hierarchical visual feature extractor with recurrent sequence model. The model is end to end trainable with capability of capturing the temporal dynamics of the system. We exploit this capability to identify road intersection in a sequence of temporally consistent images. The model has been rigorously trained and tested on various different datasets. We think that our findings could be useful to model behavior of autonomous agent in the real-world.

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