Tracklet Proposal Network for Multi-Object Tracking on Point Clouds
Authors: Hai Wu, Qing Li, Chenglu Wen, Xin Li, Xiaoliang Fan, Cheng Wang
IJCAI 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | On the KITTI tracking benchmark, it attains an MOTA of 91.75%, outperforming all submitted results on the online leaderboard. |
| Researcher Affiliation | Academia | 1School of Informatics, Xiamen University 2School of Electrical Engineering and Computer Science, Louisiana State University |
| Pseudocode | No | The paper describes the algorithms in prose but does not include any pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide concrete access to source code for the methodology described. |
| Open Datasets | Yes | We evaluate our method on the KITTI tracking dataset [Geiger et al., 2012], which consists of 21 training sequences and 29 test sequences. |
| Dataset Splits | Yes | For the ablation study, we split the KITTI training set into two sub-datasets for training and validation. The sub training set consists of 11 sequences, and the validation set consists of 10 sequences. |
| Hardware Specification | Yes | We adopted the ADAM optimizer with batch size 4, learning rate 0.01 and 80 epochs on two RTX 2080 Ti GPUs to train our PC-TCNN. |
| Software Dependencies | No | The paper does not provide specific software dependencies with version numbers. |
| Experiment Setup | Yes | We adopted the ADAM optimizer with batch size 4, learning rate 0.01 and 80 epochs on two RTX 2080 Ti GPUs to train our PC-TCNN. |