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Deep Learning-Based Crowd Scene Analysis Survey
| Content Provider | MDPI |
|---|---|
| Author | ElBishlawi, Sherif Abdelpakey, Mohamed H. Eltantawy, Agwad Shehata, Mohamed S. Mohamed, Mostafa M. |
| Copyright Year | 2020 |
| Description | Recently, our world witnessed major events that attracted a lot of attention towards the importance of automatic crowd scene analysis. For example, the COVID-19 breakout and public events require an automatic system to manage, count, secure, and track a crowd that shares the same area. However, analyzing crowd scenes is very challenging due to heavy occlusion, complex behaviors, and posture changes. This paper surveys deep learning-based methods for analyzing crowded scenes. The reviewed methods are categorized as (1) crowd counting and (2) crowd actions recognition. Moreover, crowd scene datasets are surveyed. In additional to the above surveys, this paper proposes an evaluation metric for crowd scene analysis methods. This metric estimates the difference between calculated crowed count and actual count in crowd scene videos. |
| Starting Page | 95 |
| e-ISSN | 2313433X |
| DOI | 10.3390/jimaging6090095 |
| Journal | Journal of Imaging |
| Issue Number | 9 |
| Volume Number | 6 |
| Language | English |
| Publisher | MDPI |
| Publisher Date | 2020-09-11 |
| Access Restriction | Open |
| Subject Keyword | Journal of Imaging Transportation Science and Technology Crowd Scene Crowd Counting Crowd Action Recognition Deep Learning |
| Content Type | Text |
| Resource Type | Article |
