The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.
Fast multiple objects detection and tracking fusing color camera and 3D LIDAR for intelligent vehicles
Abstract
For many robotics and intelligent vehicle applications, detection and tracking multiple objects (DATMO) is one of the most important components. However, most of the DATMO applications have difficulty in applying real-world applications due to high computational complexity. In this paper, we propose an efficient DATMO framework that fully employs the complementary information from the color camera and the 3D LIDAR. For high efficiency, we present a segmentation scheme by using both 2D and 3D information which gives accurate segments very quickly. In our experiments, we show that our framework can achieve the faster speed (~4Hz) than the state-of-the-art methods reported in KITTI benchmark (>1Hz).
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Using occupancy maps as a frame for all sensors to contribute to and then doing data association in that frame is common, especially when using multiple sensors.
“Using occupancy maps as a frame for all sensors to contribute to and then doing data association in that frame is common, especially when using multiple sensors [188].”
From A Survey of Autonomous Driving: Common Practices and Emerging Technologies (2020) · cites this paper as [188]
Citations
Cites
Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks
Ren, S., He, K., Girshick, R., & Sun, J. (2017) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2016.2577031 · Cited by 73,472
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Sorted by citations
A Survey of Autonomous Driving: Common Practices and Emerging Technologies
Yurtsever, E., Lambert, J., Carballo, A., & Takeda, K. (2020) · IEEE Access · doi:10.1109/access.2020.2983149 · Cited by 1,889
“algorithms [184] are often employed, which ensures tracking algorithms can recover from poor data association at any single time step.”
BackgroundApplication of region-based video surveillance in smart cities using deep learning
Zahra, A., Ghafoor, M., Munir, K., Ullah, A., & Ul Abideen, Z. (2021) · Multimedia Tools and Applications · doi:10.1007/s11042-021-11468-w · Cited by 53
Metadata
- Authors
- Hwang, S., Kim, N., Choi, Y., Lee, S., & Kweon, I. S.
- Affiliations
- Korea Advanced Institute of Science and Technology (KR)
- Venue
- 2016 13th International Conference on Ubiquitous Robots and Ambient Intelligence (URAI)
- Publisher
- IEEE
- Published
- 2016-08
- Pages
- 234-239
- OpenAlex
- W2543063933
- Cited by
- 46
- References
- 1
- Citations by year
- 2026: 1 · 2024: 3 · 2023: 3 · 2022: 4 · 2021: 6 · 2020: 10 · 2019: 7 · 2018: 7 · 2017: 5
- FWCI
- 8.19
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- International Conference on Ubiquitous Robots and Ambient Intelligence (publishedVersion)
- Topics
- Physical Sciences › Engineering › Automotive Engineering › Autonomous Vehicle Technology and Safety
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Video Surveillance and Tracking Methods
- Keywords
- RGB camera, 3D LiDAR, sensor fusion, real-time object tracking, intelligent vehicles, computational efficiency
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