https://github.com/allrivertosea/lidar-based-multi-object-tracking
Implement multi-target tracking based on the LiDAR data from the nuScenes dataset. Use the Kalman Filter for state estimation and the Nearest Neighbor (NN) matching method for association. Evaluate the tracking performance using the MOTA metric and visualize the tracking results in the BEV (Bird's Eye View) perspective.
https://github.com/allrivertosea/lidar-based-multi-object-tracking
Science Score: 13.0%
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○Scientific vocabulary similarity
Low similarity (3.8%) to scientific vocabulary
Repository
Implement multi-target tracking based on the LiDAR data from the nuScenes dataset. Use the Kalman Filter for state estimation and the Nearest Neighbor (NN) matching method for association. Evaluate the tracking performance using the MOTA metric and visualize the tracking results in the BEV (Bird's Eye View) perspective.
Basic Info
- Host: GitHub
- Owner: allrivertosea
- Language: C++
- Default Branch: main
- Size: 4.99 MB
Statistics
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
- Releases: 0
Metadata Files
README.md
Lidar-based-Multi-Object-Tracking
Implement multi-target tracking based on the LiDAR data from the nuScenes dataset. Use the Kalman Filter for state estimation and the Nearest Neighbor (NN) matching method for association. Evaluate the tracking performance using the MOTA metric and visualize the tracking results in the BEV (Bird's Eye View) perspective.
跟踪结果
环境说明
- Eigen 3.4.0
- Opencv 4.5.5
- Cmake 3.16.3
使用说明
mkdir build
cd build
cmake ..
make -j8
./lidar_mot scene-xxxx
Owner
- Login: allrivertosea
- Kind: user
- Repositories: 1
- Profile: https://github.com/allrivertosea
GitHub Events
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- Push event: 1
Last Year
- Watch event: 1
- Push event: 1