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Fast pedestrian detection using BWLSD for ROI | IEEE Conference Publication | IEEE Xplore

Fast pedestrian detection using BWLSD for ROI


Abstract:

This paper presents a pedestrian detection approach based on Multi-block Local Binary Pattern (MB-LBP) features and Weighted Region Covariance Matrix (WRCM). Multistage c...Show More

Abstract:

This paper presents a pedestrian detection approach based on Multi-block Local Binary Pattern (MB-LBP) features and Weighted Region Covariance Matrix (WRCM). Multistage classifiers are used to increase the processing speed and reliability of the detection system. Using the modified 3-D B-spline Wavelet-Based Local Standard Deviation (BWLSD) techniques, the region of interest is determined. Once the pedestrian region is identified, the front end of the multistage classifier quickly determines wherever pedestrians may be present, while the back end confirms whether the first descriptor did classify correctly. The experimental results demonstrated that our approach performed well in real-time application.
Date of Conference: 16-18 May 2013
Date Added to IEEE Xplore: 02 December 2013
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Conference Location: Chongqing, China

References

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