Name
Abstract | ||
|---|---|---|
Crowd counting is one of the most paramount tasks for safety and security. Many existing methods mainly focus on the predicted accuracy but ignore the efficiency, which hinders their applications in practice. Moreover, their performance heavily depends on the learning from a large number of labeled scene data, which is cost-expensive for crowd counting. In this paper, we present a novel crowd counting approach called semi-supervised manifold embedding (SSME) to address the above weaknesses. In the newly proposed method, we formulate the crowd counting as a semi-supervised classification problem and learn a linear mapping from the high-dimensional scene feature space to the low-dimension label space by simultaneously imposing the label fitness and the manifold smoothness, where the learned linear mapping facilitates the efficiency of crowd counting. In order to alleviate the issue that most supervised approaches to crowd counting require sufficient labeled data for improving the performance, we exploit the first neighbor propagation to select informative samples in the proposed SSME-based algorithm. Thorough validation experiments on three challenging benchmark datasets indicate that the proposed method is capable of achieving more impressive prediction accuracy on the number of pedestrians in a monitoring scene than other state-of-the-art competitors. (C) 2020 Elsevier B.V. All rights reserved. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1016/j.asoc.2020.106634 | APPLIED SOFT COMPUTING |
Keywords | DocType | Volume |
Crowd counting, Manifold embedding, Semi-supervised learning, Neighbor propagation | Journal | 96 |
ISSN | Citations | PageRank |
1568-4946 | 0 | 0.34 |
References | Authors | |
0 | 6 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Kaibing Zhang | 1 | 568 | 23.60 |
| Huake Wang | 2 | 0 | 0.68 |
| Wei Liu | 3 | 27 | 9.03 |
| Minqi Li | 4 | 2 | 1.71 |
| Jian Lu | 5 | 0 | 1.69 |
| Zhonghua Liu | 6 | 115 | 11.12 |