Name
Abstract | ||
|---|---|---|
In this paper, we propose a novel label distribution approach named part-based gradation regularization (PGR) for pedestrian retrieval in sensor networks. Considering different importance of various body parts, we present a gradual function to assign pedestrian label for each horizontal part. In this way, we can conduct part-based supervised learning using the identification network. The proposed PGR not only learns the discriminative local convolutional neural network-based features, but also considers the significance of assigning pedestrian label for different horizontal parts. Experimental results show that the proposed PGR obtains better performance than other approaches on three pedestrian retrieval databases, i.e., Market-1501, CUHK03, and DukeMTMC-reID databases. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1109/ACCESS.2018.2854830 | IEEE ACCESS |
Keywords | Field | DocType |
Sensor networks,pedestrian retrieval,local CNN-based features | Pattern recognition,Convolutional neural network,Computer science,Robustness (computer science),Supervised learning,Feature extraction,Regularization (mathematics),Artificial intelligence,Gradation,Wireless sensor network,Discriminative model,Distributed computing | Journal |
Volume | ISSN | Citations |
6 | 2169-3536 | 0 |
PageRank | References | Authors |
0.34 | 0 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Shuang Liu | 1 | 36 | 22.95 |
| Xiaolong Hao | 2 | 0 | 1.01 |
| Zhong Zhang | 3 | 141 | 32.42 |