Generative optical flow based abnormal object detection method using a spatio-temporal translation network (시공간 변환 네트워크를 사용한 생성 옵티컬플로우 기반 이상객체 검지 기법)

HyunSeok LIM (임현석); Jeonghwan Gwak (곽정환)

doi:10.9708/jksci.2021.26.04.011

Generative optical flow based abnormal object detection method using a spatio-temporal translation network

Journal of The Korea Society of Computer and Information
Abbr : JKSCI
2021, 26(4), pp.11-19
DOI : 10.9708/jksci.2021.26.04.011
Publisher : The Korean Society Of Computer And Information
Research Area : Engineering > Computer Science
Received : March 23, 2021
Accepted : April 16, 2021
Published : April 30, 2021

HyunSeok LIM ¹, Jeonghwan Gwak ¹

¹한국교통대학교

Accredited

ABSTRACT

An abnormal object refers to a person, an object, or a mechanical device that performs abnormal and unusual behavior and needs observation or supervision. In order to detect this through artificial intelligence algorithm without continuous human intervention, a method of observing the specificity of temporal features using optical flow technique is widely used. In this study, an abnormal situation is identified by learning an algorithm that translates an input image frame to an optical flow image using a Generative Adversarial Network (GAN). In particular, we propose a technique that improves the pre-processing process to exclude unnecessary outliers and the post-processing process to increase the accuracy of identification in the test dataset after learning to improve the performance of the model's abnormal behavior identification. UCSD Pedestrian and UMN Unusual Crowd Activity were used as training datasets to detect abnormal behavior. For the proposed method, the frame-level AUC 0.9450 and EER 0.1317 were shown in the UCSD Ped2 dataset, which shows performance improvement compared to the models in the previous studies.

KEYWORDS

Abnormal object detection, Generative adversarial network, Dense optical flow, Image-to-image translation, Image preprocessing

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* References for papers published after 2023 are currently being built.

[confproc] A. Das K. M. / 2017 / Optical Flow Based Anomaly Detection in Traffic Scenes / 2017 IEEE International Conference on Computational Intelligence and Computing Research (ICCIC) : 1~7

[confproc] Y. Gao / 2018 / Anomaly Detection for Videos of Crowded Scenes based on Optical Flow Information / 2018 3rd International Conference on Advanced Robotics and Mechatronics (ICARM) : 869~879

[journal] E. Duman / 2019 / Anomaly Detection in Videos Using Optical Flow and Convolutional Autoencoder / IEEE Access 7 : 183914~183923

[confproc] M. Ravanbakhsh / 2017 / Abnormal event detection in videos using generative adversarial nets / 2017 IEEE International Conference on Image Processing (ICIP) : 1577~1581

[confproc] M. Ravanbakhsh / 2019 / Training Adversarial Discriminators for Cross-Channel Abnormal Event Detection in Crowds / 2019 IEEE Winter Conference on Applications of Computer Vision (WACV) : 1896~1904

[confproc] W. Liu / 2018 / Future Frame Prediction for Anomaly Detection-A New Baseline / 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition : 6536~6545

[journal] I. Goodfellow / 2014 / Generative Adversarial Nets / Proc. Adv. Neural Inf. Process. Syst : 26722680~

[journal] M. Ravanbakhsh / 2017 / Abnormal event detection in videos using generative adversarial nets / IEEE Int. Conf. Image Process. (ICIP) : 15771581~

[confproc] D. Pathak / 2016 / Context Encoders: Feature Learning by Inpainting / 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) : 2536~2544

[confproc] P. Isola / 2017 / Image-to-Image Translation with Conditional Adversarial Networks / 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) : 5967~5976

[confproc] Ravanbakhsh / 2017 / Abnormal event detection in videos using generative adversarial nets / 2017 IEEE International Conference on Image Processing : 1577~1581

[confproc] V. Mahadevan / 2010 / Anomaly Detection in Crowded Scenes / IEEE Conference on Computer Vision and Pattern Recognition (CVPR)

[web] R. Mehran / Abnormal crowd behavior detection using social force model / IEEE Conf. Comput. Vis. Pattern Recognit

This paper was written with support from the National Research Foundation of Korea.

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Journal of The Korea Society of Computer and Information 2023 KCI Impact Factor : 0.65

Generative optical flow based abnormal object detection method using a spatio-temporal translation network

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Citation status

* References for papers published after 2023 are currently being built.

Journal of The Korea Society of Computer and Information 2023 KCI Impact Factor : 0.65

Generative optical flow based abnormal object detection method using a spatio-temporal translation network

ABSTRACT

KEYWORDS

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* References for papers published after 2023 are currently being built.