Automated Driving

Learning to tell brake and turn signals in videos using CNN‑LSTM structure

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TRI Author: Kuan-Hui Lee

All Authors: Han-Kai Hsu, Yi-Hsuan Tsai, Xue Mei, Kuan-Hui Lee, Naoki Nagasaka, Danil Prokhorov, Ming-Hsuan Yang

We present a method that learns to tell rear signals from a number of frames using a deep learning framework. The proposed framework extracts spatial features with a convolution neural network (CNN), and then applies a long short term memory (LSTM) network to learn the long-term dependencies. The brake signal classifier is trained using RGB frames, while the turn signal is recognized via a two-step localization approach. The two separate classifiers are learned to recognize the static brake signals and the dynamic turn signals. As a result, our recognition system can recognize 8 different rear signals via the combined two classifiers in real-world traffic scenes. Experimental results show that our method is able to obtain more accurate predictions than using only the CNN to classify rear signals with time sequence inputs. Read More

Citation: Hsu, Han-Kai, Yi-Hsuan Tsai, Xue Mei, Kuan-Hui Lee, Naoki Nagasaka, Danil Prokhorov, and Ming-Hsuan Yang. "Learning to tell brake and turn signals in videos using cnn-lstm structure." In 2017 IEEE 20th International Conference on Intelligent Transportation Systems (ITSC), pp. 1-6. IEEE, 2017.

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