Name
Abstract | ||
|---|---|---|
Crowd counting is a concerned yet challenging task in computer vision. The difficulty is particularly pronounced by scale variations in crowd images. Most state-of-art approaches tackle the multi-scale problem by adopting multicolumn CNN architectures where different columns are designed with different filter sizes to adapt to variable pedestrian/object sizes. However, the structure is bloated and inefficient, and it is infeasible to adopt multiple deep columns due to the huge resource cost. We instead propose a Scale Pyramid Network (SPN) which adopts a shared single deep column structure and extracts multi-scale information in high layers by Scale Pyramid Module. In Scale Pyramid Module, we specifically employ different rates of dilated convolutions in parallel instead of traditional convolutions with different sizes. Compared to other methods of coping with scale issues, our single column structure with Scale Pyramid Module can get more accurate estimation with simpler structure and less complexity of training. And our Scale Pyramid Module can be easily applied to a deep network. Experimental results on four datasets show that our method achieves state-of-the-art performance. On Shanghai-Tech Part A dataset which is challenging for its highly congested scenes and scale variation, we achieve 9.5% lower MAE and 13.5% lower MSE than the previous state-of-the-art method. We also extend our model on TRANCOS vehicle counting dataset and significantly achieve 5.9% lower GAME(0), 10% lower GAME(1), 24.5% lower GAME(2), 38.7% lower GAME(3) than the previous state-of-the-art method. The experimental results prove the robustness of our model for crowd counting, especially with scale variations. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1109/WACV.2019.00211 | 2019 IEEE WINTER CONFERENCE ON APPLICATIONS OF COMPUTER VISION (WACV) |
Field | DocType | ISSN |
Computer vision,Task analysis,Pattern recognition,Computer science,Convolution,Vehicle counting,Feature extraction,Robustness (computer science),Artificial intelligence,Pyramid,Crowd counting,Scale variation | Conference | 2472-6737 |
Citations | PageRank | References |
5 | 0.41 | 0 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Xinya Chen | 1 | 6 | 0.76 |
| Yanrui Bin | 2 | 5 | 0.75 |
| Nong Sang | 3 | 475 | 72.22 |
| Changxin Gao | 4 | 188 | 38.01 |