An Empirical Evaluation of Cross-scene Crowd Counting Performance
| Title | An Empirical Evaluation of Cross-scene Crowd Counting Performance |
| Publication Type | Conference Paper |
| Year of Publication | 2020 |
| Authors | Delussu, R, Putzu, L, Fumera, G |
| Conference Name | Proceedings of the 15th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - VISAPP |
| Volume | 4 |
| Pagination | 373-380 |
| Date Published | 03/2020 |
| Conference Location | Valletta - Malta |
| ISSN Number | 2184-4321 |
| ISBN Number | 978-989-758-402-2 |
| Keywords | Cross-scene Evaluation, Crowd Counting, Crowd Density Estimation, Video Surveillance |
| Abstract | Crowd counting and density estimation are useful but also challenging tasks in many video surveillance sys-
tems, especially in cross-scene settings with dense crowds, if the target scene significantly differs from the
ones used for training. This also holds for methods based on Convolutional Neural Networks (CNNs) which
have recently boosted the performance of crowd counting systems, but nevertheless require massive amounts
of annotated and representative training data. As a consequence, when training data is scarce or not rep-
resentative of deployment scenarios, also CNNs may suffer from over-fitting to a different extent, and may
hardly generalise to images coming from different scenes. In this work, we focus on real-world, challenging
application scenarios when no annotated crowd images from a given target scene are available, and evaluate
the cross-scene effectiveness of several regression-based state-of-the-art crowd counting methods, including
CNN-based ones, through extensive cross-data set experiments. Our results show that some of the existing
CNN-based approaches are capable of generalising to target scenes which differ from the ones used for train-
ing in the background or lighting conditions, whereas their effectiveness considerably degrades under different
perspective and scale.
|
| DOI | 10.5220/0008983003730380 |
| Citation Key | 1458 |
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