Computer Science ›› 2020, Vol. 47 ›› Issue (4): 119-124.doi: 10.11896/jsjkx.190300392

• Computer Graphics & Multimedia • Previous Articles     Next Articles

Motion Feature Descriptor for Abnormal Behavior Detection

WANG Kun-lun, LIU Wen-can, HE Xiao-hai, QING Lin-bo, WU Xiao-hong   

  1. School of Electronics and Information Engineering,Sichuan University,Chengdu 610065,China
  • Received:2019-03-01 Online:2020-04-15 Published:2020-04-15
  • Contact: HE Xiao-hai,born in 1964,Ph.D,professor,Ph.D supervisor.His main research interests include image processing,pattern recognition,computer vision,image communication,and software engineering.
  • About author:WANG Kun-lun,born in 1995,postgraduate.His main research interests include computer vision and pattern recognition.
  • Supported by:
    This work was supported by the Sichuan Science and Technology Program(2018HH0143),National Natural Science Foundation of China (61871278) and Chengdu Science and Technology Project (2016-XT00-00015-GX)

Abstract: Modern motion description techniques for crowd motion in videos are mostly velocity descriptors based on optical flow.However,acceleration contains a wealth of motion information,which can provide information that the velocity descriptors are missing when describing complex motion patterns,and can better characterize complex motion patterns.This paper studies a motion descriptor,which uses an energy-based restricted Boltzmann machine model to perform anomalous behavior detection.Firstly,the optical flow information in the video is extracted,and the acceleration information is calculated through the optical flow information of two consecutive frames.Then,acceleration histogram feature is computed over spatial-temporal blocks,and all the spatial-temporal block histogram features of adjacent frames are spliced to obtain an acceleration descriptor.The Restricted Boltzmann Machine learns the normal motion patterns from the normal video training set,which is used for abnormal detection in terms of the errors of reconstructed data in detecting phase.The results show that the average area under the curve (AUC) of the UMN dataset reaches 0.984,and the area under the average curve (AUC) of UCF-Web reaches 0.958.Compared with other state-of-the-art algorithms,the proposed descriptor has superior performance on anomaly detection.

Key words: Abnormal behavior, Acceleration optical flow, Feature extraction, Motion feature, Restricted boltzmann machine

CLC Number: 

  • TP391.41
[1]SODEMANN A A,ROSS M P,BORGHETTI B J.A review of anomaly detection in automated surveillance[J].IEEE Transactions on Systems,Man,and Cybernetics,Part C (Applications and Reviews),2012,42(6):1257-1272.
[2]ILG E,MAYER N,SAIKIA T,et al.Flownet 2.0:Evolution of optical flow estimation with deep networks[C]//IEEEConfe-rence on Computer Vision and Pattern Recognition.IEEE,2017:6.
[3]YANG Y,SOATTO S.Conditional Prior Networks for OpticalFlow[C]// European Conference on Computer Vision.Sprin-ger,2018:282-298.
[4]KLASER A,MARSZALEK M,SCHMID C.A spatio-temporaldescriptor based on 3d-gradients[C]//19th British Machine Vision Conference.British Machine Vision Association,2008:275:1-10.
[5]NISHINO K,KRATZ L.Anomaly detection in extremelycrowded scenes using spatio-temporal motion pattern models[C]//2009 IEEE Conference on Computer Vision and Pattern Recognition.Miami,IEEE,2009:1446-1453.
[6]LAPTEV I,MARSZALEK M,SCHMID C,et al.Learning realistic human actions from movies[C]//Computer Vision and Pattern Recognition.IEEE,2008:1-8.
[7]CONG Y,YUAN J,LIU J.Sparse reconstruction cost for abnormal event detection[C]//Computer Vision and Pattern Recognition.IEEE,2011:3449-3456.
[8]LI A,MIAO Z,CEN Y,et al.Abnormal event detection based on sparse reconstruction in crowded scenes[C]//Acoustics,Speech and Signal Processing (ICASSP).IEEE,2016:1786-1790.
[9]NALLAIVAROTHAYAN H,FOOKES C,DENMAN S,et al.An MRF based abnormal event detection approach using motion and appearance features[C]//Advanced Video and Signal Based Surveillance (AVSS).IEEE,2014:343-348.
[10]DIREKOGLU C,SAH M,O’CONNOR N E.Abnormal crowd behavior detection using novel optical flow-based features[C]//2017 14th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS).IEEE,2017:1-6.
[11]EDISON A,JIJI C V.Optical Acceleration for Motion Description in Videos[C]//Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW).Honolulu,2017:21-26.
[12]HINTON G E,SALAKHUTDINOV R R.Reducing the dimensionality of data with neural networks[J].Science,2006,313(5786):504-507.
[13]VU H,NGUYEN T D,TRAVERS A,et al.Energy-based localized anomaly detection in video surveillance[C]//Pacific-Asia Conference on Knowledge Discovery and Data Mining.Springer.Cham,2017:641-653.
[14]ACKLEY D H,HINTON G E,SEJNOWSKI T J.A learning algorithm for Boltzmann machines[J].Cognitive Science,1985,9(1):147-169.
[15]MEHRAN R,OYAMA A,SHAH M.Abnormal crowd behavior detection using social force model[C]//Computer Vision and Pattern Recognition.IEEE,2009:935-942.
[16]WANG T,QIAO M,DENG Y,et al.Abnormal event detection based on analysis of movement information of video sequence[J].Optik-International Journal for Light and Electron Optics,2018,152:50-60.
[17]SHI Y,GAO Y,WANG R.Real-time abnormal event detection in complicated scenes[C]//Pattern Recognition (ICPR).IEEE,2010:3653-3656.
[18]WANG T,SNOUSSI H.Detection of abnormal visual events via global optical flow orientation histogram[J].IEEE Transactions on Information Forensics and Security,2014,9(6):988-998.
[19]ZHU X,LIU J,WANG J,et al.Sparse representation for robust abnormality detection in crowded scenes[J].Pattern Recognition,2014,47(5):1791-1799.
[20]ZHU X,LIU J,WANG J,et al.Weighted interaction force estimation for abnormality detection in crowd scenes[C]//Asian Conference on Computer Vision.Springer,Berlin,Heidelberg,2012:507-518.
[21]CHEN H,GAI J,ZHANG S,et al.Abnormal event detection based on cosparse reconstruction[J].The Journal of Enginee-ring,2018,2018(5):254-256.
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