Authors:
Hadi Keivan Ekbatani
;
Oriol Pujol
and
Santi Segui
Affiliation:
University of Barcelona, Spain
Keyword(s):
Synthetic Data Generation, Deep Convolutional Neural Network, Deep Learning, Computer Vision.
Related
Ontology
Subjects/Areas/Topics:
Applications
;
Artificial Intelligence
;
Biomedical Engineering
;
Biomedical Signal Processing
;
Computational Intelligence
;
Computer Vision, Visualization and Computer Graphics
;
Geometry and Modeling
;
Health Engineering and Technology Applications
;
Human-Computer Interaction
;
Image Understanding
;
Image-Based Modeling
;
Methodologies and Methods
;
Missing Data
;
Neural Networks
;
Neurocomputing
;
Neurotechnology, Electronics and Informatics
;
Pattern Recognition
;
Physiological Computing Systems
;
Regression
;
Sensor Networks
;
Signal Processing
;
Soft Computing
;
Software Engineering
;
Theory and Methods
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.