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.
|Title of host publication||2017 IEEE 20th International Conference on Intelligent Transportation Systems, ITSC 2017|
|Publisher||Institute of Electrical and Electronics Engineers Inc.|
|Number of pages||6|
|Publication status||Published - 2018 Mar 14|
|Event||20th IEEE International Conference on Intelligent Transportation Systems, ITSC 2017 - Yokohama, Kanagawa, Japan|
Duration: 2017 Oct 16 → 2017 Oct 19
|Name||IEEE Conference on Intelligent Transportation Systems, Proceedings, ITSC|
|Conference||20th IEEE International Conference on Intelligent Transportation Systems, ITSC 2017|
|Period||17/10/16 → 17/10/19|
Bibliographical notePublisher Copyright:
© 2017 IEEE.
All Science Journal Classification (ASJC) codes
- Automotive Engineering
- Mechanical Engineering
- Computer Science Applications