Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in [1].
UCMCTrack: Multi-Object Tracking with Uniform Camera Motion Compensation
Authors: Kefu Yi, Kai Luo, Xiaolei Luo, Jiangui Huang, Hao Wu, Rongdong Hu, Wei Hao
AAAI 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We conducted a fair evaluation of UCMCTrack on multiple publicly available datasets, including MOT17 (Milan et al. 2016), MOT20 (Dendorfer et al. 2020), Dance Track (Sun et al. 2022), and KITTI (Geiger et al. 2013). Ablation Studies on UCMC |
| Researcher Affiliation | Collaboration | 1School of Traffic and Transportation, Changsha University of Science and Technology 2College of Automotive and Mechanical Engineering, Changsha University of Science and Technology 3Changsha Intelligent Driving Institute |
| Pseudocode | Yes | For the pseudocode please refer to Appendix A. |
| Open Source Code | Yes | More details and code are available at https://github.com/ corfyi/UCMCTrack. |
| Open Datasets | Yes | We conducted a fair evaluation of UCMCTrack on multiple publicly available datasets, including MOT17 (Milan et al. 2016), MOT20 (Dendorfer et al. 2020), Dance Track (Sun et al. 2022), and KITTI (Geiger et al. 2013). |
| Dataset Splits | Yes | For MOT17, the validation set was split following the prevailing conventions (Zhou, Koltun, and Kr ahenb uhl 2020). |
| Hardware Specification | No | The paper mentions running at 1000 FPS using "just a single CPU", but does not provide specific hardware details (like CPU model, GPU, or memory) used for training or running experiments. |
| Software Dependencies | No | The paper mentions using YOLOX and Byte Track, but does not provide specific version numbers for these or any other software dependencies. |
| Experiment Setup | No | The paper describes general implementation details such as the object detection method and weight files used, and the camera motion compensation model, but does not provide specific hyperparameters (e.g., learning rate, batch size, number of epochs) or detailed training configurations. |