Learning Cross-View Binary Identities for Fast Person Re-Identification
Authors: Feng Zheng, Ling Shao
IJCAI 2016 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments are conducted on two public datasets and CBI produces comparable results as state-of-the-art re-identification approaches but is at least 2200 times faster. |
| Researcher Affiliation | Academia | 1Department of Electronic and Electrical Engineering, The University of Sheffield. 2Department of Computer Science and Digital Technologies, Northumbria University. |
| Pseudocode | Yes | Algorithm 1 CBI training |
| Open Source Code | Yes | The codes are released on a website: https://sites.google.com/site/crossmodalhashing/re-identification |
| Open Datasets | Yes | We test our proposed CBI for person re-identification on two public datasets: VIPe R [Gray and Tao, 2008] and CUHK01 [Li et al., 2014]. |
| Dataset Splits | Yes | We randomly partition a dataset into two parts without overlap on person identities, according to a certain percentage. The expectation is reported by conducting 10 trials of evaluation. |
| Hardware Specification | Yes | All algorithms are run on a Matlab 7 platform installed on Windows 7 with Intel Core 3.4GHz CPU and 8G memory. |
| Software Dependencies | Yes | All algorithms are run on a Matlab 7 platform installed on Windows 7 with Intel Core 3.4GHz CPU and 8G memory. |
| Experiment Setup | Yes | CBI is not sensitive to the parameters for the two datasets and we set λ1 = 2 and C = 200 for all the experiments. However, λ2 will be set to 0.05, 10 and 5 for ELF, SCNCD and LOMO, respectively. |