Abnormal Crowd Motion Detection Using Double Sparse Representations

Jiang, Jinzhe; Tao, Ye; Zhao, Wei; Tang, Xianglong

doi:10.1007/978-3-319-23989-7_26

Cited by 1 publication

(2 citation statements)

References 22 publications

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“…In recent years, in order to improve the performance of crowd abnormal behavior detection caused by explosions, terrorist attacks, and other emergencies, many crowd abnormal behavior detection algorithms based on video sequences have been proposed. ese algorithms can be roughly divided into two categories: visual feature extraction method and physical feature analysis method [10][11][12][13][14][15][16][17][18][19]. e former uses vision and image processing technology to extract crowd features and then detect anomalies.…”

Section: Introductionmentioning

confidence: 99%

“…is method can intuitively reflect the shape of the crowd, but because of its single information and incomplete features, it has problems such as poor accuracy, low training efficiency, and limited data processing capabilities. In order to improve the behavior characteristics and the detection accuracy, literature [12] proposed a crowd abnormal behavior detection based on Bayesian model (BM). e concept of potential object and divergence center was introduced to represent crowd movement tendency so as to improve the integrity of behavior characteristics.…”

Section: Introductionmentioning

confidence: 99%

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Crowd Counting and Abnormal Behavior Detection via Multiscale GAN Network Combined with Deep Optical Flow

Song¹,

Sheng²

2020

Mathematical Problems in Engineering

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Aiming at the problem of low performance of crowd abnormal behavior detection caused by complex backgrounds and occlusions, this paper proposes a single-image crowd counting and abnormal behavior detection via multiscale GAN network. The proposed method firstly designed an embedded GAN module with a multibranch generator and a regional discriminator to initially generate crowd-density maps; and then our proposed multiscale GAN module is added to further strengthen the generalization ability of the model, which can effectively improve the accuracy and robustness of the prediction detection and counting. On the basis of single-image crowd counting, synthetic optical-flow feature descriptor is adopted to obtain the crowd motion trajectory, and the classification of abnormal behavior is finally implemented. The simulation results show that the proposed algorithm can significantly improve the accuracy and robustness of crowd counting and abnormal behavior detection in real complex scenarios compared with the existing mainstream algorithms, which is suitable for engineering applications.

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