Person Reidentification Based on Pose-Invariant Feature and B-KNN Reranking
| Autor: | Shengrong Gong, Shan Zhong, Zongming Bao, Kaijian Xia |
|---|---|
| Rok vydání: | 2021 |
| Předmět: |
Color constancy
Artificial neural network business.industry Computer science Deep learning Feature extraction ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION Pattern recognition Field (computer science) Ranking (information retrieval) Human-Computer Interaction Discriminative model Modeling and Simulation Artificial intelligence business Social Sciences (miscellaneous) Image restoration |
| Zdroj: | IEEE Transactions on Computational Social Systems. 8:1272-1281 |
| ISSN: | 2373-7476 |
| DOI: | 10.1109/tcss.2021.3063318 |
| Popis: | Person reidentification, as one of the most important areas in the video surveillance field, is a crucial task in computer vision. It has attracted more and more attention in both academic research and industry due to its extremely high application value. However, the recognition accuracy of person reidentification is subjected to factors, such as illumination, pose, occlusion, and viewpoint. To alleviate the effect of such factors, the multiscale Retinex with color restoration (MSRSC) algorithm is adopted to preprocess the original images so that the color information can be restored and the illumination condition can be improved. To obtain pose-invariant features (PIFs), the convolution pose machine that can generate the body joint points of pedestrians is applied to divide the body into seven parts, the pose transform network is then used to align the body parts, and finally, the PIFs can be obtained from a designed pseudo-Siamese network by using the original and aligned images as the training samples. To further improve recognition accuracy, a reranking method based on bidirectional k-nearest neighbors (KNN) is presented to optimize the ranking list. Experimentally, the proposed method is implemented on three data sets: viewpoint invariant pedestrian recognition (VIPeR), CUHK03, and Market1501. The results demonstrate our method outperforms the other methods, as a result of both the representation of a more discriminative feature descriptor and the introduction of a reranking method. |
| Databáze: | OpenAIRE |
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