Paper Title:
An Improved Pedestrian Detection Method Based on Adaboost and Performance Analysis
  Abstract

Pedestrian detection, which has a wide application in surveillance, advanced robotics, and especially intelligent vehicles, is an important area in computer vision. This paper applies a detection approach based on improved Adaboost algorithm. We use a dataset to train the weak classifiers (with different numbers) to cascade to be strong classifiers, in which we employ optimized strategy of sample weight adjustment to reduce the over-fit. After constructing a strong classifier, we apply different scale of sliding widow to shift and calculate the corresponding features to classify them as pedestrians or non-pedestrians. The experiments show that different numbers of weak classifiers layer and different scale of sliding windows can give different performance in detecting.

  Info
Periodical
Advanced Materials Research (Volumes 317-319)
Chapter
Machine Vision
Edited by
Xin Chen
Pages
877-880
DOI
10.4028/www.scientific.net/AMR.317-319.877
Citation
Y. X. Zhang, X. D. Gao, "An Improved Pedestrian Detection Method Based on Adaboost and Performance Analysis", Advanced Materials Research, Vols. 317-319, pp. 877-880, 2011
Online since
August 2011
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Price
$32.00
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