Name
Abstract | ||
|---|---|---|
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. |
Year | Venue | Field |
|---|---|---|
2017 | 2017 IEEE 20TH INTERNATIONAL CONFERENCE ON INTELLIGENT TRANSPORTATION SYSTEMS (ITSC) | Computer vision,Brake,Recognition system,Convolutional neural network,Long short term memory,Artificial intelligence,RGB color model,Deep learning,Engineering,Classifier (linguistics) |
DocType | ISSN | Citations |
Conference | 2153-0009 | 0 |
PageRank | References | Authors |
0.34 | 0 | 7 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Han-Kai Hsu | 1 | 2 | 0.69 |
| Yi-Hsuan Tsai | 2 | 138 | 18.08 |
| Xue Mei | 3 | 793 | 22.88 |
| Kuan-Hui Lee | 4 | 83 | 10.04 |
| Naoki Nagasaka | 5 | 0 | 0.34 |
| Danil V. Prokhorov | 6 | 374 | 37.68 |
| Yang Ming-Hsuan | 7 | 15303 | 620.69 |