Robust Estimation of Similarity Transformation for Visual Object Tracking
Authors: Yang Li, Jianke Zhu, Steven C.H. Hoi, Wenjie Song, Zhefeng Wang, Hantang Liu8666-8673
AAAI 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our experimental results demonstrate that the proposed tracker achieves very promising prediction performance compared with the state-of-the-art visual object tracking methods while still retaining the advantages of high efficiency and simplicity in conventional correlation filter-based tracking methods. In this section, we conduct four different experiments to evaluate our proposed tracker LDES comprehensively. |
| Researcher Affiliation | Collaboration | Yang Li,1 Jianke Zhu,*1,3 Steven C.H. Hoi,2 Wenjie Song,1 Zhefeng Wang,1 Hantang Liu1 1Colleage of Computer Science and Technology, Zhejiang University, Hangzhou, China 2School of Information Systems, Singapore Management University, Singapore 3Alibaba-Zhejiang University Joint Research Institute of Frontier Technologies |
| Pseudocode | No | No structured pseudocode or algorithm blocks explicitly labeled as such were found. |
| Open Source Code | Yes | The main contributions of our work are summarized as follows: ... 3) a new approach for scale and rotation estimation with efficient implementation which can improve a family of existing correlation filter-based trackers (our implementation is available at https://github.com/ihpdep/LDES). |
| Open Datasets | Yes | We first evaluate our proposed log-polar based scale estimator on OTB-2013 and OTB-100 dataset (Wu, Lim, and Yang 2013; 2015). To better evaluate our proposed approach to rotation estimation, we conduct an additional experiment on POT benchmark (Liang, Wu, and Ling 2018). |
| Dataset Splits | No | The paper uses standard benchmarks (OTB-2013, OTB-100, POT) but does not explicitly describe the training, validation, or test splits used for these benchmarks within the text. |
| Hardware Specification | Yes | All the methods were implemented in Matlab and the experiments were conducted on a PC with an Intel i7-4770 3.40GHz CPU and 16GB RAM. |
| Software Dependencies | No | The paper states 'All the methods were implemented in Matlab' but does not provide a version number for Matlab or any other specific software libraries or dependencies with their versions. |
| Experiment Setup | Yes | Experimental Settings All the methods were implemented in Matlab and the experiments were conducted on a PC with an Intel i7-4770 3.40GHz CPU and 16GB RAM. We employ Ho G feature for both translational and scale-rotation estimation, and the extra color histogram is used to estimate translational. All patch is multiplied a Hann window as suggested in (Bolme et al. 2010). η is 0.15 and λ is set to 1e 4. λφ and λα are both set to 0.01. λω is 0.015. The size of learning patch D is 2.2 larger than the original target size. Moreover, the searching window size N is about 1.5 larger than the learning patch size D. For scale-rotation estimation, the phase correlation sample size is about 1.8 larger than the original target size. All parameters are fixed in the following experiments. |