Analysis of Regionlets for Pedestrian Detection

Human detection in camera images is an important task for many autonomous robots as well as automated driving systems. The Regionlets detector was one of the best-performing approaches for pedestrian detection on the KITTI dataset when we started this work in 2015. We analysed the Regionlets detector and its performance. This paper discusses the improvements in accuracy that were achieved by the different ideas of the Regionlets detector. It also analyses what the boosting algorithm learns and how this relates to the expectations. We found that the random generation of regionlet configurations can be replaced by a regular grid of regionlets. Doing so reduces the dimensionality of the feature space drastically but does not decrease detection performance. This translates into a decrease in memory consumption and computing time during training.

[1]  Ammad Ali,et al.  Face Recognition with Local Binary Patterns , 2012 .

[2]  Yoram Singer,et al.  Improved Boosting Algorithms Using Confidence-rated Predictions , 1998, COLT' 98.

[3]  Ming Yang,et al.  Regionlets for Generic Object Detection , 2013, 2013 IEEE International Conference on Computer Vision.

[4]  Koen E. A. van de Sande,et al.  Segmentation as selective search for object recognition , 2011, 2011 International Conference on Computer Vision.

[5]  Bernt Schiele,et al.  Semantic Modeling of Natural Scenes for Content-Based Image Retrieval , 2007, International Journal of Computer Vision.

[6]  David A. McAllester,et al.  A discriminatively trained, multiscale, deformable part model , 2008, 2008 IEEE Conference on Computer Vision and Pattern Recognition.

[7]  Shihong Lao,et al.  Boosting nested cascade detector for multi-view face detection , 2004, ICPR 2004.

[8]  Benjamin Ranft,et al.  Modeling arbitrarily oriented slanted planes for efficient stereo vision based on block matching , 2014, 17th International IEEE Conference on Intelligent Transportation Systems (ITSC).

[9]  Andreas Geiger,et al.  Are we ready for autonomous driving? The KITTI vision benchmark suite , 2012, 2012 IEEE Conference on Computer Vision and Pattern Recognition.