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].
Learning Robust Gaussian Process Regression for Visual Tracking
Authors: Linyu Zheng, Ming Tang, Jinqiao Wang
IJCAI 2018 | Venue PDF | 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 ฮด in section 3.2 to 0.004 and 0.007 for our GPRTE and GPRT, respectively. Meanwhile, we set the learning and search ratio ฯ in section 3.2 to 4 for both GPRTE and GPRT. For accuracy and speed, we resize the target in the ๏ฌrst frame to ensure the minimum and maximum area are 1000 and 4000 pixels, respectively... In gaussian kernel, we set ฯf = 1.0 and ฯn = 0.01... we set it to 1.4 on all sequences. |