Advanced SearchSearch Tips
Cascade Selective Window for Fast and Accurate Object Detection
facebook(new window)  Pirnt(new window) E-mail(new window) Excel Download
 Title & Authors
Cascade Selective Window for Fast and Accurate Object Detection
Zhang, Shu; Cai, Yong; Xie, Mei;
  PDF(new window)
Several works help make sliding window object detection fast, nevertheless, computational demands remain prohibitive for numerous applications. This paper proposes a fast object detection method based on three strategies: cascade classifier, selective window search and fast feature extraction. Experimental results show that the proposed method outperforms the compared methods and achieves both high detection precision and low computation cost. Our approach runs at 17ms per frame on 640×480 images while attaining state-of-the-art accuracy.
Object detection;Cascade;Adaboost;Selective window search;
 Cited by
Real-time vehicle detection with foreground-based cascade classifier, IET Image Processing, 2016, 10, 4, 289  crossref(new windwow)
Pedro Felzenszwalb, Ross Girshick, David Mc-Allester and Deva Ramanan, “Object detection with discriminatively trained part-based models,” IEEE Trans. Pattern Anal. Mach. Intel, vol. 32, no. 9, pp. 1627-1645, 2010. crossref(new window)

Lubomir Bourdev and Jonathan Brandt, “Robust object detection via soft cascade,” Computer Vision and Pattern Recognition, Colorado, America, 2005.

Piotr Dollar, Christian Wojek, Bernt Schiele, and Pietro Perona, “Pedestrian detection: An evaluation of the state of the art,” IEEE Trans. Pattern Anal. Mach. Intel, vol. 34, no 4, pp. 743-760, 2012. crossref(new window)

Nicholas Butko and Javier Movellan, “Optimal scanning for faster object detection,” Computer Vision and Pattern Recognition, Miami, America, 2009.

Paul Viola and Michael Jones, “Rapid object detection using a boosted cascade of simple features,” Computer Vision and Pattern Recognition, Kauai Hawaii, 2001.

Giovanni Gualdi, Andrea Prati, and Rita Cucchiara, “A multi-stage pedestrian detection using monolithic classifiers,” Advanced Video and Signal Based Surveillance, Klagenfurt, Austria, 2011.

Piotr Dollar, Serge Belongie and Pietro Perona, “The fastest pedestrian detector in the west,” British Machine Vision Conference, Aberystwyth, UK, 2010.

Christoph Lampert, Matthew Blaschko and Thomas Hofmann, “Efficient subwindow search: A branch and bound framework for object localization,” IEEE Trans. Pattern Anal. Mach. Intel, vol.31, no.12, pp. 2129-2142, 2009. crossref(new window)

Marco Pedersoli, Jordi Gonzàlez, Andrew Bagdano and Juan Villanueva, “Recursive coarse-to-fine localization for fast object detection,” European Conference on Computer Vision, Heraklion, Crete, Greece , 2010.

Wei Zhang, Gregory Zelinsky and Dimitris Samaras, “Real-time accurate object detection using multiple resolutions,” International Conference on Computer Vision, Rio de Janeiro, Brazil, 2007.

Richard Baraniuk, Mark Davenport, Ronald DeVore and Michael Wakin, “A simple proof of the restricted isometry property for random matrices,” Constructive Approximation, vol.28, no.3, pp.253-263, 2008. crossref(new window)

Ping Li, Trevor Hastie and Kenneth Church, "Very sparse random projections," Knowledge Discovery and Data Mining, New York, USA, 2006.

Rong Fan, Kai Chang, Cho Hsieh, Xiang Wang and Chih Lin, “Liblinear: A library for large linear classification,” Journal of Machine Learning Research, vol. 9, pp. 1871-1874, 2008.

Navneet Dalal and Bill Triggs, “Histograms of oriented gradients for human detection,” Computer Vision and Pattern Recognition, SanDiego, USA, 2005.