Deep social force network for anomaly event detection
Abstract Anomaly event detection is vital in surveillance video analysis. However, how to learn the discriminative motion in the crowd scene is still not tackled. Here, a deep social force network by exploiting both social force extracting and deep motion coding is proposed. Given a grid of particle...
Enregistré dans:
Auteurs principaux: | Xingming Yang, Zhiming Wang, Kewei Wu, Zhao Xie, Jinkui Hou |
---|---|
Format: | article |
Langue: | EN |
Publié: |
Wiley
2021
|
Sujets: | |
Accès en ligne: | https://doaj.org/article/f9b737b35a6a4726b5ad426c877f8b3f |
Tags: |
Ajouter un tag
Pas de tags, Soyez le premier à ajouter un tag!
|
Documents similaires
-
Anomaly detection in video sequences: A benchmark and computational model
par: Boyang Wan, et autres
Publié: (2021) -
A deep learning method for video‐based action recognition
par: Guanwen Zhang, et autres
Publié: (2021) -
Multi‐label learning based target detecting from multi‐frame data
par: Mengqing Mei, et autres
Publié: (2021) -
MFNet‐LE: Multilevel fusion network with Laplacian embedding for face presentation attacks detection
par: Sijie Niu, et autres
Publié: (2021) -
Multi‐dimensional weighted cross‐attention network in crowded scenes
par: Yefan Xie, et autres
Publié: (2021)