Swiss-System Based Cascade Ranking for Gait-Based Person Re-Identification
Authors: Lan Wei, Yonghong Tian, Yaowei Wang, Tiejun Huang
AAAI 2015 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments on three indoor and outdoor public datasets demonstrate that our model outperforms several state-of-the-art methods remarkably. |
| Researcher Affiliation | Academia | 1School of EE & CS, Peking University, Beijing 100871, China 2Department of Electronic Engineering, Beijing Institute of Technology, Beijing 100081, China |
| Pseudocode | Yes | Algorithm 1 Swiss-system based cascade ranking algorithm |
| Open Source Code | No | The paper does not provide any statements or links regarding the release of the source code for the described methodology. |
| Open Datasets | Yes | Extensive experiments have been carried out on the three gait databases: CASIA, Soton and PKU Human ID. As shown in Fig. 4, they cover an indoor environment (CASIA), outdoor (Soton) and no controlled scenario (PKU). |
| Dataset Splits | No | The paper mentions dividing subjects into training and testing sets but does not specify a separate validation set or describe how validation was performed. |
| Hardware Specification | No | The paper does not mention specific hardware components (e.g., CPU/GPU models, memory) used for running the experiments. |
| Software Dependencies | No | The paper does not list specific software dependencies with version numbers (e.g., library names like PyTorch, TensorFlow with their versions). |
| Experiment Setup | No | The paper describes the experimental settings (e.g., datasets, splits, comparison methods), but it does not provide concrete hyperparameter values or detailed system-level training configurations (e.g., learning rate, batch size, optimizer details). |