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- W2890208103 abstract "The existing pedestrian detection algorithms are mostly based on 2D image processing, which are susceptible to complex background, the changes of light intensity, and many other factors. For these reasons, the robustness and accuracy of traditional algorithms are not ideal. Moreover, for other few algorithms that use 3D vision, the core process is to extract features by converting 3D images to grayscale images, which cannot make full use of depth information. To solve these problems, a pedestrian detection algorithm based on depth image is proposed in this paper. For depth image preprocessing, a 3D dilation and erosion algorithm based on probability density is proposed. Combined with the Head Ternary Pattern-Head Shoulder Density (HTP-HSD) features proposed in this paper, the implement of pedestrian detection can be ensured using SVM classifier to classify the features. Experimental results show that the HTP-HSD features meet the actual needs of pedestrian detection with higher recognition rate and recognition speed." @default.
- W2890208103 created "2018-09-27" @default.
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- W2890208103 date "2018-01-01" @default.
- W2890208103 modified "2023-09-27" @default.
- W2890208103 title "Head Ternary Pattern-Head Shoulder Density Features Pedestrian Detection Algorithm Based on Depth Image" @default.
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- W2890208103 doi "https://doi.org/10.1007/978-981-13-2384-3_50" @default.
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