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- W2896523943 abstract "In this paper, we first extract three different kinds of high-level features from LIDAR point cloud, and combine them into the DHA (Depth, Height and Angle) channels. Integrated with the traditional RGB image from camera, we build a rich feature-based road object classifier by training a deep convolutional neural network model with six-channel (RGBDHA) data. Subsequently, this deep convolution neural network is fed by the integration of spacial and RGB information. With additional upsampled LIDAR data, the classifier reaches higher accuracy than single RGB image base methods. Several simulations on the famous autonomous vehicle benchmark of KITTI show that our fusion-based classifier outperforms RGB-based approaches about 15% and reaches average accuracy of 96%." @default.
- W2896523943 created "2018-10-26" @default.
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- W2896523943 date "2018-07-01" @default.
- W2896523943 modified "2023-10-02" @default.
- W2896523943 title "DHA: Lidar and Vision data Fusion-based On Road Object Classifier" @default.
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- W2896523943 doi "https://doi.org/10.1109/ijcnn.2018.8489732" @default.
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