%0 Conference Paper %1 8369680 %A Tatebe, Y. %A Deguchi, D. %A Kawanishi, Y. %A Ide, I. %A Murase, H. %A Sakai, U. %B 2018 International Workshop on Advanced Image Technology (IWAIT) %D 2018 %K dnn lidar order1 pedestrian %P 1-4 %R 10.1109/IWAIT.2018.8369680 %T Pedestrian detection from sparse point-cloud using 3DCNN %U https://ieeexplore.ieee.org/abstract/document/8369680 %X This paper proposes a LIDAR-based pedestrian detection method using 3DCNN. The proposed method converts a sparse point-cloud obtained by a low-resolution LIDAR to two-channels voxel representation that consists of the 3D object probability channel and the reflection intensity channel. To evaluate the performance of the proposed method, an experiment using real-world LIDAR data was conducted. The results show that the proposed method is able to detect pedestrians more accurately than detectors trained by other conventional features.

@inproceedings{8369680, abstract = {This paper proposes a LIDAR-based pedestrian detection method using 3DCNN. The proposed method converts a sparse point-cloud obtained by a low-resolution LIDAR to two-channels voxel representation that consists of the 3D object probability channel and the reflection intensity channel. To evaluate the performance of the proposed method, an experiment using real-world LIDAR data was conducted. The results show that the proposed method is able to detect pedestrians more accurately than detectors trained by other conventional features.}, added-at = {2020-05-27T23:38:46.000+0200}, author = {{Tatebe}, Y. and {Deguchi}, D. and {Kawanishi}, Y. and {Ide}, I. and {Murase}, H. and {Sakai}, U.}, biburl = {https://www.bibsonomy.org/bibtex/20205912371af68db19562a13839f7c54/sohnki}, booktitle = {2018 International Workshop on Advanced Image Technology (IWAIT)}, description = {Pedestrian detection from sparse point-cloud using 3DCNN - IEEE Conference Publication}, doi = {10.1109/IWAIT.2018.8369680}, interhash = {165bbb4da569c2a8ee0d6be1f8062c65}, intrahash = {0205912371af68db19562a13839f7c54}, keywords = {dnn lidar order1 pedestrian}, month = jan, pages = {1-4}, timestamp = {2020-06-02T20:02:09.000+0200}, title = {Pedestrian detection from sparse point-cloud using 3DCNN}, url = {https://ieeexplore.ieee.org/abstract/document/8369680}, year = 2018 }