Crowd abnormal behavior identification method based on SURF (Speed-Up Robust Feature) stream and LLE (Locally Linear Embedding) sparse representation

A sparse representation and recognition method technology, applied in the field of computer vision, can solve problems such as high dimensionality of behavioral features, unstable local feature structure, and large amount of data

Active Publication Date: 2014-04-02
CHINA JILIANG UNIV
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Problems solved by technology

[0006] The crowd abnormal behavior recognition method based on SURF flow and local linear embedding sparse representation of the present invention mainly solves the problems of inaccu

Method used

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  • Crowd abnormal behavior identification method based on SURF (Speed-Up Robust Feature) stream and LLE (Locally Linear Embedding) sparse representation
  • Crowd abnormal behavior identification method based on SURF (Speed-Up Robust Feature) stream and LLE (Locally Linear Embedding) sparse representation
  • Crowd abnormal behavior identification method based on SURF (Speed-Up Robust Feature) stream and LLE (Locally Linear Embedding) sparse representation

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Embodiment

[0043] The recognition process of the test video sequence is as follows: figure 1 As shown, a group of videos shot by a Sony HVR-V1C camera on campus, each frame in the video has an image size of 360*240, and the video contains four groups of behaviors: normal, fighting, panic, and stampede. 200 frames of each behavior are selected as sample video sequences, and 100 frames are selected as test video sequences.

[0044] Input the test video and the training video sequence, first perform the Hessian matrix interest point detection on each frame of the video sequence:

[0045]

[0046] Discriminant:

[0047] In order to facilitate the application, the value of I is approximated by box filter and image convolution, and the 9×9 box filter template corresponds to the second-order Gaussian filter .

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Abstract

The invention discloses a crowd abnormal behavior identification method based on SURF stream and LLE sparse representation. The method is mainly used for solving the problems that crowd characteristics extraction in the complex scene is not accurate, the behavior characteristics dimension in the crowd behavior detection is high, the data volume is large and the local manifold structure of the characteristics is unstable. The method comprises the following steps: (1) inputting a test video sample and a training video sample, creating a SURF stream field and acquiring characteristic point motion vector information; (3) respectively classifying the characteristic point vector information of each frame in the test video sequence and the training video sequence into 216-dimensional characteristics, and enabling the video sequence to form behavior characteristic set; (3) utilizing a locally linear embedding sparse representation formula to classify the characteristic set, and obtaining a sparse representation coefficient; (4) computing a residual error and judging the category of the test video. The crowd abnormal behavior identification method can effectively remain the local manifold structure of the test sample, and improve the judgment capability to the sample.

Description

technical field [0001] The invention belongs to the field of computer vision, in particular to a crowd abnormal behavior recognition method based on SURF flow and local linear embedding (LLE) sparse representation. Background technique [0002] In recent years, crowd behavior recognition, as one of the important topics of computer vision, has been applied in intelligent video surveillance and popularized in public security, financial security, transportation, and other fields. Video-based abnormal crowd behavior detection technology has become a relatively active branch in this field, and has attracted the attention and research of many scholars at home and abroad in recent years. [0003] Crowd behavior detection mainly considers the description of crowd behavior characteristics and the classification and judgment of abnormal behavior. Among them, the detection of crowd behavior mainly considers the motion characteristics of crowd behavior in video sequences, such as opti...

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Application Information

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IPC IPC(8): G06K9/00G06K9/46
Inventor 章东平徐凯航潘晨彭怀亮
Owner CHINA JILIANG UNIV
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