Name
Abstract | ||
|---|---|---|
One of the main limitations of the application of Deep Learning (DL) algorithms is when dealing with problems with small data. One workaround to this issue is the use of synthetic data generators. In this framework, we explore the benefits of synthetic data generation as a surrogate for the lack of large data when applying DL algorithms. In this paper, we propose a problem of learning to count the number of pedestrians using synthetic images as a substitute for real images. To this end, we introduce an algorithm to create synthetic images for being fed to a designed Deep Convolutional Neural Network (DCNN) to learn from. The model is capable of accurately counting the number of individuals in a real scene. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.5220/0006119203180323 | ICPRAM: PROCEEDINGS OF THE 6TH INTERNATIONAL CONFERENCE ON PATTERN RECOGNITION APPLICATIONS AND METHODS |
Keywords | Field | DocType |
Synthetic Data Generation, Deep Convolutional Neural Network, Deep Learning, Computer Vision | Synthetic data generation,Computer science,Artificial intelligence,Deep learning,Machine learning | Conference |
Citations | PageRank | References |
2 | 0.37 | 0 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Hadi Keivan Ekbatani | 1 | 2 | 0.37 |
| Oriol Pujol | 2 | 963 | 60.82 |
| Santi Seguí | 3 | 85 | 9.11 |