AONet: Active Offset Network for crowd flow prediction. (January 2021)
- Record Type:
- Journal Article
- Title:
- AONet: Active Offset Network for crowd flow prediction. (January 2021)
- Main Title:
- AONet: Active Offset Network for crowd flow prediction
- Authors:
- Wang, Dafeng
Ma, Qian
Wang, Naiyao
Fan, Xuanzhe
Lu, Mingyu
Liu, Hongbo - Abstract:
- Abstract: Predicting crowd flow is of great importance to public safety and traffic management. The crowd flow is difficult to predict accurately and timely due to the uncertainty of the future positions. In this paper, we propose a novel Active Offset Network (AONet), in which ActiveGRU (Active Gate Recurrent Unit) is designed to predict the variation of pedestrians' positions in the crowd flow. Its inner location-variant recurrent structure is implemented by utilizing convolution operation on low dimensional spatio-temporal sequences to obtain fractional offset locations. Afterwards, the sampling locations are determined by bilinear interpolation on fractional offset locations. Moreover, a probabilistic sparse strategy is introduced to reduce the links between sampling locations during supervised training. Finally, the experiments over popular benchmarks demonstrate that our method can actively characterize the future positions of pedestrians. Meanwhile, the performance of the proposed AONet is superior over state-of-art baselines with regard to both accuracy and computational savings.
- Is Part Of:
- Engineering applications of artificial intelligence. Volume 97(2021)
- Journal:
- Engineering applications of artificial intelligence
- Issue:
- Volume 97(2021)
- Issue Display:
- Volume 97, Issue 2021 (2021)
- Year:
- 2021
- Volume:
- 97
- Issue:
- 2021
- Issue Sort Value:
- 2021-0097-2021-0000
- Page Start:
- Page End:
- Publication Date:
- 2021-01
- Subjects:
- Crowd flow -- Bilinear interpolation -- Location-variant -- Probabilistic sparse
Engineering -- Data processing -- Periodicals
Artificial intelligence -- Periodicals
Expert systems (Computer science) -- Periodicals
Ingénierie -- Informatique -- Périodiques
Intelligence artificielle -- Périodiques
Systèmes experts (Informatique) -- Périodiques
Artificial intelligence
Engineering -- Data processing
Expert systems (Computer science)
Periodicals
620.00285 - Journal URLs:
- http://www.sciencedirect.com/science/journal/09521976 ↗
http://www.elsevier.com/journals ↗ - DOI:
- 10.1016/j.engappai.2020.104022 ↗
- Languages:
- English
- ISSNs:
- 0952-1976
- Deposit Type:
- Legaldeposit
- View Content:
- Available online (eLD content is only available in our Reading Rooms) ↗
- Physical Locations:
- British Library DSC - 3755.704500
British Library DSC - BLDSS-3PM
British Library HMNTS - ELD Digital store - Ingest File:
- 14985.xml