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Novel pedestrian detection method based on coarse-to-finerepresentation strategy(PDF)


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Novel pedestrian detection method based on coarse-to-finerepresentation strategy
Ren Hanjun1Song Xiaoning1Yu Dongjun2
1.School of IoT Engineering,Jiangnan University,Wuxi 214122,China; 2.School of ComputerScience and Engineering,Nanjing University of Science and Technology,Nanjing 210094,China
pedestrian detection locally decorrelated channel feature color self-similarity feature convolutional network architecture log-average miss rate
In order to suppress too many false detection windows in pedestrian detection,a novel coarse-to-fine representation strategy is proposed based on the locally decorrelated channel features(LDCF)method.The LDCF method is used for coarse detection to generate a set of candidate windows with the high recall rate,and the improved color self-similarity feature extraction method and the simplified convolutional network architecture are introduced to extract more discriminant fusion features over these windows.Finally,the cascade classifier is applied for the fine classification of candidate windows.Experimental results show that,compared with the traditional LDCF method,the log-average miss rates of the proposed method on the INRIA and the Caltech databases are reduced by 2.81% and 3.85% respectively,demonstrating merits of the coarse-to-fine strategy and robustness of extracted features.


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