Abnormal event detection in traffic video surveillance based on local features

2011 ◽  
Author(s):  
Lili Cui ◽  
Kehuang Li ◽  
Jiapin Chen ◽  
Zhenbo Li
Keyword(s):  
Video Surveillance ◽  
Event Detection ◽  
Local Features ◽  
Abnormal Event Detection ◽  
Traffic Video

Clustering Based on Two Layers for Abnormal Event Detection in Video Surveillance

2019 ◽  
pp. 433-453
Author(s):  
Emna Fendri ◽  
Najla Bouarada Ghrab ◽  
Mohamed Hammami
Keyword(s):  
Video Surveillance ◽  
Hierarchical Clustering ◽  
Event Detection ◽  
Clustering Algorithms ◽  
Experimental Results ◽  
Robust Method ◽  
Agglomerative Hierarchical Clustering ◽  
Abnormal Event Detection ◽  
Research Attention

Abnormal event detection has attracted great research attention in video surveillance. In this paper, the authors presented a robust method of trajectories clustering for abnormal event detection. This method is based on two layers and benefits from two well-known clustering algorithms: the agglomerative hierarchical clustering and the k-means clustering. Facing to the challenges related to the trajectories, e.g., different sizes, the authors introduce a preprocessing step to unify their sizes and reduce their dimensionality. The experimental results show the performance and accuracy of their proposed method.

Pedestrian abnormal event detection based on multi-feature fusion in traffic video

Optik ◽  
2018 ◽  
Vol 154 ◽  
pp. 22-32 ◽  
Author(s):  
Xuan Wang ◽  
Huansheng Song ◽  
Hua Cui
Keyword(s):  
Event Detection ◽  
Feature Fusion ◽  
Abnormal Event Detection ◽  
Traffic Video

Clustering Based on Two Layers for Abnormal Event Detection in Video Surveillance

2017 ◽  
Vol 5 (4) ◽  
pp. 1-18
Author(s):  
Emna Fendri ◽  
Najla Bouarada Ghrab ◽  
Mohamed Hammami
Keyword(s):  
Video Surveillance ◽  
Hierarchical Clustering ◽  
Event Detection ◽  
Clustering Algorithms ◽  
Experimental Results ◽  
Robust Method ◽  
Agglomerative Hierarchical Clustering ◽  
Abnormal Event Detection ◽  
Research Attention

Abnormal event detection has attracted great research attention in video surveillance. In this paper, the authors presented a robust method of trajectories clustering for abnormal event detection. This method is based on two layers and benefits from two well-known clustering algorithms: the agglomerative hierarchical clustering and the k-means clustering. Facing to the challenges related to the trajectories, e.g., different sizes, the authors introduce a preprocessing step to unify their sizes and reduce their dimensionality. The experimental results show the performance and accuracy of their proposed method.

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