Name
Abstract | ||
|---|---|---|
In this paper, we present a learning-based brake light classification algorithm for intelligent driver-assistance systems. State-of-the-art approaches apply different image processing techniques with hand-crafted features to determine whether brake lights are on or off. In contrast, we learn a brake light classifier based on discriminative color descriptors and convolutional features fine-tuned for traffic scenes. We show how brake light regions can be segmented and classified in one framework. Numerous experimental results show that the proposed algorithm performs well against state-of-the-art alternatives in real-world scenes. |
Year | Venue | Field |
|---|---|---|
2016 | 2016 IEEE 19TH INTERNATIONAL CONFERENCE ON INTELLIGENT TRANSPORTATION SYSTEMS (ITSC) | Computer vision,Brake,Colors of noise,Simulation,Image processing,Feature extraction,Vehicle detection,Artificial intelligence,Engineering,Classifier (linguistics),Discriminative model |
DocType | Citations | PageRank |
Conference | 0 | 0.34 |
References | Authors | |
0 | 7 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Guangyu Zhong | 1 | 0 | 0.34 |
| Yi-Hsuan Tsai | 2 | 138 | 18.08 |
| Yi-Ting Chen | 3 | 11 | 4.20 |
| Xue Mei | 4 | 793 | 22.88 |
| Danil V. Prokhorov | 5 | 374 | 37.68 |
| Michael R. James | 6 | 10 | 1.95 |
| Yang Ming-Hsuan | 7 | 15303 | 620.69 |