Abnormal event detection method based on low-rank approximation structured sparse representation

A technology of sparse representation and abnormal events, applied in computer parts, instruments, computing, etc., can solve the problems of insufficient mining of low-rank characteristics and inherent structural redundancy of video data, and achieve accurate semantic understanding of dynamic scenes and accurate sparse reconstruction. , the effect of improving the detection speed

Inactive Publication Date: 2017-03-15
NANJING UNIV OF SCI & TECH
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Problems solved by technology

Most sparse representation models obtain an over-complete dictionary through training, but do n

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  • Abnormal event detection method based on low-rank approximation structured sparse representation
  • Abnormal event detection method based on low-rank approximation structured sparse representation
  • Abnormal event detection method based on low-rank approximation structured sparse representation

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[0046] The present invention will be described in further detail below in conjunction with the accompanying drawings.

[0047] Abnormal event detection method of the present invention includes three main processes of feature extraction process, training process and testing process, such as figure 1 shown.

[0048] The feature extraction process is as figure 2 shown, including the following specific steps:

[0049] Video sequence image frames are converted into 3D image pyramid process 21 . Convert each frame of the video sequence to a grayscale image, and scale each frame of the grayscale image to three different scales: 20×20, 30×40 and 120×160, forming a three-level image pyramid. For each scale of the image pyramid, each frame is divided into non-overlapping regions of the same spatial size (10×10), such as image 3 shown.

[0050] The process of extracting the 3D gradient feature of the video sequence 22. In order to take into account the morphological features and ...

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Abstract

Disclosed in the invention is an abnormal event detection method based on low-rank approximation structured sparse representation. The method comprises three steps of feature extraction, training and testing. To be specific, step (1), a multi-scale three-dimensional gradient feature of a video sequence is extracted; step two, dimensionality reduction is carried out on the multi-scale three-dimensional gradient feature, thereby forming a training feature set and testing feature set; step three, a residual training feature and a correlated parameter are initialized; step four, iterative learning group sparse dictionary is carried out on the residual training feature, thereby obtaining a normal-mode dictionary; step five, sparse reconstruction is carried out on a testing feature by using the group sparse dictionary obtained by the training process; and step six, according to a reconstruction error, whether the testing feature is an abnormal feature is determined. Therefore, defects that the low-rank characteristic of video data is not dug fully and the detection is carried out slowly according to the abnormality detection technology can be solved.

Description

technical field [0001] The invention relates to the fields of pattern recognition and video analysis, and more specifically, relates to a method for detecting abnormal events based on low-rank approximation structured sparse representation. Background technique [0002] Anomaly event detection in video sequences is an active research topic in computer vision and has been widely used in many applications, such as crowd monitoring, public place detection, traffic safety, and abnormal personal behavior. In the face of massive video data, the traditional manual marking of abnormal events is time-consuming and inefficient. Therefore, automated and fast anomaly detection methods for video sequences are urgently needed. [0003] Although research on anomalous event detection has made great progress in feature extraction, behavior modeling, and anomaly measurement, anomalous event detection in video sequences is still a very challenging task. First, there is no precise definition ...

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Application Information

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IPC IPC(8): G06K9/00G06K9/46G06K9/62
CPCG06V20/42G06V10/513G06F18/23213
Inventor 刘亚洲余博思刘柯柯孙权森
Owner NANJING UNIV OF SCI & TECH
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