A High Accuracy Pedestrian Detection System Combining a Cascade AdaBoost Detector and Random Vector Functional-Link Net
Autor: | Zhihui Wang, Sook Yoon, Shan Juan Xie, Yu Lu, Dong Sun Park |
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Jazyk: | angličtina |
Rok vydání: | 2014 |
Předmět: | |
Zdroj: | The Scientific World Journal, Vol 2014 (2014) |
Druh dokumentu: | article |
ISSN: | 2356-6140 1537-744X |
DOI: | 10.1155/2014/105089 |
Popis: | In pedestrian detection methods, their high accuracy detection rates are always obtained at the cost of a large amount of false pedestrians. In order to overcome this problem, the authors propose an accurate pedestrian detection system based on two machine learning methods: cascade AdaBoost detector and random vector functional-link net. During the offline training phase, the parameters of a cascade AdaBoost detector and random vector functional-link net are trained by standard dataset. These candidates, extracted by the strategy of a multiscale sliding window, are normalized to be standard scale and verified by the cascade AdaBoost detector and random vector functional-link net on the online phase. Only those candidates with high confidence can pass the validation. The proposed system is more accurate than other single machine learning algorithms with fewer false pedestrians, which has been confirmed in simulation experiment on four datasets. |
Databáze: | Directory of Open Access Journals |
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