scholarly journals Research on Abnormal Event Detection in Video Surveillance Based on Displacement of Feature Point and GLCM Texture Features

2018 ◽  
Author(s):  
Yue WANG ◽  
Xue-jun ZHANG ◽  
Jin-wen DENG ◽  
Mu-jun LIU
Keyword(s):  
Video Surveillance ◽  
Event Detection ◽  
Texture Features ◽  
Feature Point ◽  
Abnormal Event Detection

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.

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.

scholarly journals A Novel Framework for Anomaly Detection in Video Surveillance using Convolutional LSTM

2020 ◽  
Vol 9 (4) ◽  
pp. 355-359
Keyword(s):  
Object Tracking ◽  
Video Surveillance ◽  
Event Detection ◽  
Video Data ◽  
Surveillance Systems ◽  
Abnormal Event Detection ◽  
Suggested Approach ◽  
Processing Technologies ◽  
Safety Requirements ◽  
Convolutional Lstm

Today, due to public safety requirements, surveillance systems have gained increased attention. Video data processing technologies such as the identification of activity [1], object tracking [2], crowd counting [3], and the detection of anomalies [ 4] have therefore been rapidly developing. In this study, we establish an unattended method for the detection of anomaly events in videos based on a ConvLSTM encoder-decoder to learn about the evolution of spatial characteristics. Our model only covers typical video events during preparation, whereas in testing the videos are both usual and abnormal. Experiments on the UCSD datasets confirm the validity of the suggested approach to abnormal event detection.

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