Name
Title | ||
|---|---|---|
Semi-supervised Learning: Fusion of Self-supervised, Supervised Learning, and Multimodal Cues for Tactical Driver Behavior Detection. |
Abstract | ||
|---|---|---|
In this paper, we presented a preliminary study for tactical driver behavior detection from untrimmed naturalistic driving recordings. While supervised learning based detection is a common approach, it suffers when labeled data is scarce. Manual annotation is both time-consuming and expensive. To emphasize this problem, we experimented on a 104-hour real-world naturalistic driving dataset with a set of predefined driving behaviors annotated. There are three challenges in the dataset. First, predefined driving behaviors are sparse in a naturalistic driving setting. Second, the distribution of driving behaviors is long-tail. Third, a huge intra-class variation is observed. To address these issues, recent self-supervised and supervised learning and fusion of multimodal cues are leveraged into our architecture design. Preliminary experiments and discussions are reported. |
Year | Venue | Field |
|---|---|---|
2018 | arXiv: Computer Vision and Pattern Recognition | Architecture design,Semi-supervised learning,Computer science,Manual annotation,Supervised learning,Artificial intelligence,Labeled data,Machine learning |
DocType | Volume | Citations |
Journal | abs/1807.00864 | 1 |
PageRank | References | Authors |
0.35 | 0 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Athma Narayanan | 1 | 2 | 0.71 |
| Yi-Ting Chen | 2 | 11 | 4.20 |
| Srikanth Malla | 3 | 6 | 2.48 |