Learning Robust Gaussian Process Regression for Visual Tracking
Authors: Linyu Zheng, Ming Tang, Jinqiao Wang
IJCAI 2018 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments are performed on two public datasets: OTB-2013 and OTB-2015. Without bells and whistles, on these two datasets, our GPRT obtains 84.1% and 79.2% in mean overlap precision, respectively, outperforming all the existing trackers with hand-crafted features. |
| Researcher Affiliation | Academia | University of Chinese Academy of Sciences, Beijing, China National Lab of Pattern Recognition, Institute of Automation, CAS, Beijing 100190, China |
| Pseudocode | Yes | Algorithm 1 Proposed tracking framework. |
| Open Source Code | No | We will release code to facilitate future research. |
| Open Datasets | Yes | Experiments are performed on two public datasets: OTB-2013 [Wu et al., 2013] and OTB-2015 [Wu et al., 2015]. |
| Dataset Splits | No | The paper discusses 'training data' and 'test samples' in the context of online learning and detection, but it does not specify explicit train/validation/test dataset splits, percentages, or a cross-validation setup for evaluation. |
| Hardware Specification | Yes | The experiments are performed on Linux with Intel E5-2673 2.4GHz CPU and single TITAN X GPU with CUDA-8.0. |
| Software Dependencies | Yes | Our GPRTE and GPRT are both implemented under MATLAB and C++. The experiments are performed on Linux with Intel E5-2673 2.4GHz CPU and single TITAN X GPU with CUDA-8.0. |
| Experiment Setup | Yes | We set the learning rate δ in section 3.2 to 0.004 and 0.007 for our GPRTE and GPRT, respectively. Meanwhile, we set the learning and search ratio σ in section 3.2 to 4 for both GPRTE and GPRT. For accuracy and speed, we resize the target in the first frame to ensure the minimum and maximum area are 1000 and 4000 pixels, respectively... In gaussian kernel, we set σf = 1.0 and σn = 0.01... we set it to 1.4 on all sequences. |