Anomaly detection in crowded scenes using motion energy model

Anomaly detection in crowded scenes using motion energy model We present a new method for detection of abnormal behaviors in crowded scenes. Based on statistics of low-level feature—optical flow, which describes human movement efficiently, the motion energy model is proposed to represent the local motion pattern in the crowd. The model stresses the difference between normal and abnormal behaviors by considering sum of square differences (SSD) metric of motion information in the center block and its neighboring blocks. Meanwhile, data increasing rate is introduced to filter outliers to achieve boundary values between abnormal and normal motion patterns. In this model, an abnormal behavior is detected if the occurrence probability of anomaly is higher than a preset threshold, namely the motion energy value of its corresponding block is higher than that of the normal one. We evaluate the proposed method on two public available datasets, showing competitive performance with respect to state-of-the-art approaches not only in detection accuracy, but also in computational efficiency. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Multimedia Tools and Applications Springer Journals

Anomaly detection in crowded scenes using motion energy model

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Publisher
Springer US
Copyright
Copyright © 2017 by Springer Science+Business Media, LLC
Subject
Computer Science; Multimedia Information Systems; Computer Communication Networks; Data Structures, Cryptology and Information Theory; Special Purpose and Application-Based Systems
ISSN
1380-7501
eISSN
1573-7721
D.O.I.
10.1007/s11042-017-5020-3
Publisher site
See Article on Publisher Site

Abstract

We present a new method for detection of abnormal behaviors in crowded scenes. Based on statistics of low-level feature—optical flow, which describes human movement efficiently, the motion energy model is proposed to represent the local motion pattern in the crowd. The model stresses the difference between normal and abnormal behaviors by considering sum of square differences (SSD) metric of motion information in the center block and its neighboring blocks. Meanwhile, data increasing rate is introduced to filter outliers to achieve boundary values between abnormal and normal motion patterns. In this model, an abnormal behavior is detected if the occurrence probability of anomaly is higher than a preset threshold, namely the motion energy value of its corresponding block is higher than that of the normal one. We evaluate the proposed method on two public available datasets, showing competitive performance with respect to state-of-the-art approaches not only in detection accuracy, but also in computational efficiency.

Journal

Multimedia Tools and ApplicationsSpringer Journals

Published: Jul 14, 2017

References

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