Homography Decomposition Networks for Planar Object Tracking
Authors: Xinrui Zhan, Yueran Liu, Jianke Zhu, Yang Li3234-3242
AAAI 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments show that our proposed approach outperforms the state-of-the-art planar tracking methods at a large margin on the challenging POT, UCSB and POIC datasets. Experiment Setup We conducted experiments on a PC with an intel E5-2678-v3 processor (2.5GHz), 32GB RAM and Nvidia GTX 2080Ti GPU. |
| Researcher Affiliation | Collaboration | 1 Zhejiang University 2 Alibaba-Zhejiang University Joint Institute of Frontier Technologies 3 East China Normal University |
| Pseudocode | No | No pseudocode or algorithm blocks found in the paper. |
| Open Source Code | Yes | Codes and models are available at https://github.com/zhanxinrui/HDN. |
| Open Datasets | Yes | The algorithm randomly chooses x from a certain range to perform the transformation on COCO14 (Lin et al. 2014). To solve the unrealistic problem of synthetic datasets for residual component training, we sample the images of tracking dataset GOT10k (Huang, Zhao, and Huang 2019) with a small interval threshold as training data and adopt an unsupervised residual loss L Λ+. |
| Dataset Splits | No | No explicit training/validation/test split percentages or sample counts for validation are provided. The paper mentions training on COCO14 and GOT10k, and testing on POT, UCSB, and POIC, but doesn't detail specific validation splits. |
| Hardware Specification | Yes | We conducted experiments on a PC with an intel E5-2678-v3 processor (2.5GHz), 32GB RAM and Nvidia GTX 2080Ti GPU. |
| Software Dependencies | No | The paper states, 'Our proposed method is implemented in Pytorch,' but does not provide a specific version number for PyTorch or any other software dependencies. |
| Experiment Setup | Yes | For the hyperparameters of HDN in training and testing, we set λ1 = 100 , λ2 = 1, λ3 = 1, λ4 = 0.25, K = 100. γ [1/1.38, 1.38], θ [ 0.7, 0.7], t [ 32, 32], k1 [ 0.1, 0.1], k2 [ 0.015, 0.015], ν [ 0.0015, 0.0015]. The probability threshold τ in Lcls is set to 0.7. |