Appearance and Motion Enhancement for Video-Based Person Re-Identification
Authors: Shuzhao Li, Huimin Yu, Haoji Hu11394-11401
AAAI 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments conducted on three popular video-based person Re ID benchmarks demonstrate the effectiveness of our proposed model and the state-of-the-art performance compared with existing methods. |
| Researcher Affiliation | Academia | Shuzhao Li,1 Huimin Yu,1,2 Haoji Hu1 1College of Information Science and Electronic Engineering, Zhejiang University, Hangzhou, China 2The State Key Laboratory of CAD and CG, Zhejiang University, Hangzhou, China |
| Pseudocode | No | The paper does not contain any structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide any concrete access to source code for the methodology described. |
| Open Datasets | Yes | MARS (Zheng et al. 2016) is currently one of the largest video-based person re-identification datasets, which consists of 1261 identities and around 20000 human walking sequences. i LIDS-VID (Wang et al. 2014) is composed of 600 sequences belonging to 300 different pedestrians from two non-overlapping cameras. PRID-2011 (Hirzer et al. 2011) consists of 749 different identities from one camera, and 385 identities from the other, with only the first 200 people appear in both cameras. |
| Dataset Splits | Yes | We follow the original splits provided by MARS, and for i LIDS-VID and PRID-2011, we follow the evaluation protocol from previous works (Wang et al. 2014) where the dataset is randomly split into the train/test set for 10 times, then the averaged accuracies are reported. |
| Hardware Specification | Yes | We implement our proposed algorithm based on Py Torch framework on two GTX 1080Ti GPUs with 11GB memory. |
| Software Dependencies | No | We implement our proposed algorithm based on Py Torch framework on two GTX 1080Ti GPUs with 11GB memory. (No version specified for PyTorch or other libraries). |
| Experiment Setup | Yes | The initial learning rate is set to 1e-3, and decreased by 0.2 every 60 epochs. The weight decay is set to 5e-4. The length of the input sequence T is empirically set to 8. The input frames are resized to 256 128. The sizes of the feature maps in our model are set to H = 16, W = 8, C = 1024, T = 3, and the dimension of the final feature fs is set to 512. The hyperparameters k, λA, λM are set to 0.2, 0.1, 10 respectively. |