|Table of Contents|

Improved Video Anomaly Detection with Dual Criss-Cross Attention Auto Encoder(PDF)

《南京师大学报（自然科学版）》[ISSN:1001-4616/CN:32-1239/N]

Issue:
2023年01期
Page:
110-119
Research Field:
计算机科学与技术
Publishing date:

Info

Title:
Improved Video Anomaly Detection with Dual Criss-Cross Attention Auto Encoder
Author(s):
Qi Xiaosha1; Zeng Jing2; Ji Genlin2
(1.School of Mathematical Sciences, Nanjing Normal University, Nanjing 210023, China)
(2.School of Computer and Electronic Information/Artificial Intelligence, Nanjing Normal University, Nanjing 210023, China)
Keywords:
anomaly detection;  auto encoder;  frame;  reconstruction;  deep learning;  neural network;  feature extraction;  fusion
PACS:
TP391
DOI:
10.3969/j.issn.1001-4616.2023.01.015
Abstract:
To solve the problems such as sparse quantity of abnormal events contained in video and information-intensive features are easy ommitted,this paper proposes a dual criss-cross attention auto encoder for video abnormal detection. Firstly,we preprocess the video to extract the apparent and motion features in the video,then design the dual criss-cross attention module and incorporate it into auto encoder,in this way,the features can better correlate the global features. Further,we put the extracted features into the respective auto encoders to learn normal behavior,in this way,video frames containing normal events can be reconstructed by the model and those containing abnormal events cannot be reconstructed. Finally,reconstruction errors of each video frame are obtained by the model to determine the abnormal events. This method can effectively improve the accuracy of abnormal event detection by correlating global features with local features,and it is proved to be better than other similar methods through experimental validation in several public datasets.

References:

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Last Update: 2023-03-15