VCA
Video Coding & Architectures Research group





Publication details



Authors Rob Wijnhoven, Peter H. N. de With
Title Fast Training of Object Detection using Stochastic Gradient Descent
Proceedings Proc. IEEE International Conference on Pattern Recognition (ICPR)
Journal
Volume
ISBN
Pages 424-427
Date 2010-08-23
Location Istanbul, Turkey
Papers paper pdf
Abstract Training datasets for object detection problems are typically very large and Support Vector Machine (SVM) implementations are computationally complex. As opposed to these complex techniques, we use Stochastic Gradient Descent (SGD) algorithms that use only a single new training sample in each iteration and process samples in a stream-like fashion. We have incorporated SGD optimization in an object detection framework. The object detection problem is typically highly asymmetric, because of the limited variation in object appearance, compared to the background. Incorporating SGD speeds up the optimization process significantly, requiring only a single iteration over the training set to obtain results comparable to state-of-the-art SVM techniques. SGD optimization is linearly scalable in time and the obtained speedup in computation time is two to three orders of magnitude. We show that by considering only part of the total training set, SGD converges quickly to the overall optimum.
BibTeX @inproceedings{Wijnhoven2010b,
AUTHOR = "Rob Wijnhoven and Peter H. N. de With",
TITLE = "Fast Training of Object Detection using Stochastic Gradient Descent",
BOOKTITLE = "Proc. IEEE International Conference on Pattern Recognition (ICPR)",
PAGES = "424-427",
YEAR = "2010",
}