An Improved Pedestrian Detection Method Based on Adaboost and Performance Analysis

Abstract:

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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)

Edited by:

Xin Chen

Pages:

877-880

DOI:

10.4028/www.scientific.net/AMR.317-319.877

Citation:

Y. X. Zhang and 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:

$35.00

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