NDLI: Anomaly detection in crowded scenes using genetic programming

Content Provider	IEEE Xplore Digital Library
Author	Cheng Xie Lin Shang
Copyright Year	2014
Description	Author affiliation: State Key Lab. for Novel Software Technol., Nanjing Univ., Nanjing, China (Cheng Xie; Lin Shang)
Abstract	Genetic programming(GP) has become an increasingly hot issue in evolutionary computation due to its extensive application. Anomaly detection in crowded scenes is also a hot research topic in computer vision. However, there are few contributions on using genetic programming to detect abnormalities in crowded scenes. In this paper, we focus on anomaly detection in crowded scenes with genetic programming. We propose a new method called Multi-Frame LBP Difference(MFLD) based on Local Binary Patterns(LBP) to extract pixel-level features from videos without additional complex preprocessing operations such as optical flow and background subtraction. Genetic programming is employed to generate an anomaly detector with the extracted data. When a new video is coming, the detector can classify every frame and localize the abnormality to a single-pixel level in realtime. We validate our approach on a public dataset and compare our method with other traditional algorithms for video anomaly detection. Experimental results indicate that our method with genetic programming performs better in detecting abnormalities in crowded scenes.
Starting Page	1832
Ending Page	1839
File Size	775663
Page Count	8
File Format	PDF
e-ISBN	9781479914883
DOI	10.1109/CEC.2014.6900396
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2014-07-06
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Videos Feature extraction Detectors Genetic programming Sociology Statistics Testing
Content Type	Text
Resource Type	Article

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