Robust Collaborative Discriminative Learning for RGB-Infrared Tracking
Authors: Xiangyuan Lan, Mang Ye, Shengping Zhang, Pong Yuen
AAAI 2018 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments on RGB-infrared image sequences demonstrate the effectiveness of the proposed method. Extensive comparison experiments with other ten baseline methods demonstrate its effectiveness and excellent performance. |
| Researcher Affiliation | Academia | Department of Computer Science, Hong Kong Baptist University School of Computer Science and Technology, Harbin Institute of Technology xiangyuanlan@life.hkbu.edu.hk, mangye@comp.hkbu.edu.hk, s.zhang@hit.edu.cn, pcyuen@comp.hkbu.edu.hk |
| Pseudocode | Yes | Algorithm 1: Optimization Algorithm for (4) |
| Open Source Code | No | The paper does not explicitly state that the source code for the described methodology is publicly available, nor does it provide a link to a code repository. |
| Open Datasets | Yes | Sixteen video pairs2 which include videos of RGB and infrared modality under different scenarios and conditions are used to evaluate the RGB-infrared tracking performance. 2http://hcp.sysu.edu.cn/resources/ http://vcipl-okstate.org/pbvs/bench/index.html |
| Dataset Splits | No | The paper mentions 'training samples' and evaluating on '16 videos', but does not specify explicit train/validation/test dataset splits, percentages, or sample counts for reproducibility. |
| Hardware Specification | No | The paper does not explicitly describe the specific hardware (e.g., CPU, GPU models, memory) used to run the experiments. |
| Software Dependencies | No | The paper does not provide specific version numbers for any ancillary software dependencies used in the experiments. |
| Experiment Setup | Yes | We empirically set the λ1, λ2, α1, α2 and α3 in (2), the C1 and C2 in (3), η in (14) and ν in (15) to be 0.1, 0.01, 0.1, 1, 0.02, 0.1, 0.001, 0.01, 0.1, respectively. |