Improved Statistical Interference Model for Person Re-identification
- Linxuan Li
- Corresponding Author
- Linxuan Li
Available Online April 2019.
- https://doi.org/10.2991/icmeit-19.2019.12How to use a DOI?
- Person re-identification, statistical interference, metric learning.
- Person Re-identification problem is an important and challenging task in computer vision task. Due to the drastic appearance variation caused by misalignment and illumination changing, traditional metric models are failed in similarity measure of pedestrian images. In this paper, a novel metric learning based method is proposed. It establishes a probability inference model based on the probability models of positive pairs and negative pairs. And a balance parameter is proposed in the metric model to deal with the imbalance problem of samples. Finally, experiments are conducted on the VIPeR dataset compared with some metric learning based model. And the test results verified the effectiveness of the proposed model.
- Open Access
- This is an open access article distributed under the CC BY-NC license.
Cite this article
TY - CONF AU - Linxuan Li PY - 2019/04 DA - 2019/04 TI - Improved Statistical Interference Model for Person Re-identification BT - 3rd International Conference on Mechatronics Engineering and Information Technology (ICMEIT 2019) PB - Atlantis Press SN - 2352-538X UR - https://doi.org/10.2991/icmeit-19.2019.12 DO - https://doi.org/10.2991/icmeit-19.2019.12 ID - Li2019/04 ER -