New object detection features in the OpenCV library

In this work the object detection problem is considered. A short description of implementations of the object detection system with a discriminatively trained part based model and a gradient boosting trees algorithm (as part of OpenCV library) is given. Application of the gradient boosting trees learner to the object detection problem (in terms of the pedestrian detection problem) is explored.

[1]  David A. McAllester,et al.  Object Detection with Discriminatively Trained Part Based Models , 2010, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[2]  Luc Van Gool,et al.  The Pascal Visual Object Classes (VOC) Challenge , 2010, International Journal of Computer Vision.

[3]  Leo Breiman,et al.  Random Forests , 2001, Machine Learning.

[4]  Pierre Geurts,et al.  Extremely randomized trees , 2006, Machine Learning.

[5]  Bill Triggs,et al.  Histograms of oriented gradients for human detection , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05).

[6]  Dariu Gavrila,et al.  Monocular Pedestrian Detection: Survey and Experiments , 2009, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[7]  J. Friedman Greedy function approximation: A gradient boosting machine. , 2001 .

[8]  Bernhard E. Boser,et al.  A training algorithm for optimal margin classifiers , 1992, COLT '92.