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- W3143522914 abstract "A part-based mixture model(Poselets)was ultilized to alleviate the problem of pedestrian detection under occlusion and articulation.Firstly,Procrustes distances between similar pedestrian parts were calculated to gather training exmaples with the same configurations.Then,common parts of frontal pedestrian described by Histogram of Oriented Gradient(HOG)were trained to get discriminative models.A test was carried out on a validation set to find out which parts were more accurate,and those pictures which were not detected formed the hard set.Other complementary parts were explored on the hard set,afterwards,all these parts formed the final Poselets detector.While detecting,Poselets were clusterd if the symmetrized KL-divergence between two Poselet activations were small.The bounding box of a pedestrian was inferred by the bounding boxes of the Poselets in a cluster.Based on the test of the INRIA pedestrian dataset,it is showed that the detection rate increases from 77% of Poselets to81%in this paper while False Positive Per Image(FPPI)is 0.5.It is concluded that the part selection mechanism proposed in this paper promotes the detection rate with speedup rate about twice of the traditional method,meanwhile it reduces the number of models." @default.
- W3143522914 created "2021-04-13" @default.
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- W3143522914 date "2013-01-01" @default.
- W3143522914 modified "2023-09-27" @default.
- W3143522914 title "Poselets for pedestrian detection" @default.
- W3143522914 hasPublicationYear "2013" @default.
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