Name
Abstract | ||
|---|---|---|
Deep learning models have recently achieved the state-of-the-art results on a well-known pedestrian detection dataset. However, such images were obtained from open scenarios with fixed imaging geometry parameters, which may produce a network not suitable for detecting a person in more general settings, such as the ones found in surveillance systems. As gathering and annotating data is a highly expensive manual task, we propose a methodology for artificially augmenting the positive training set with automatically generated local image affine and perspective transforms. Furthermore, to enrich the variability of background images, we include to the negative training set images that resemble human figures automatically obtained by the proposed methodology over images from commonly found surveillance scenarios. Extensive results show that by providing the enriched data as the input to a Convolutional Neural Network it is possible to precisely detect pedestrians in a number of public datasets. The data enrichment proposed here may also be used in other detectors based on supervised learning architectures, as the process is independent from the learning algorithm employed. |
Year | DOI | Venue |
|---|---|---|
2016 | 10.1109/ICMLA.2016.169 | 2016 15TH IEEE INTERNATIONAL CONFERENCE ON MACHINE LEARNING AND APPLICATIONS (ICMLA 2016) |
Field | DocType | Citations |
Affine transformation,Pattern recognition,Computer science,Convolutional neural network,Supervised learning,Invariant (mathematics),Data enrichment,Artificial intelligence,Deep learning,Pedestrian detection,Detector,Machine learning | Conference | 0 |
PageRank | References | Authors |
0.34 | 0 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Cristina Nader Vasconcelos | 1 | 76 | 12.15 |
| Aline Paes | 2 | 0 | 0.34 |
| Anselmo Montenegro | 3 | 116 | 15.18 |