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Abnormal behavior identification method in error BP Adaboost network based on video motion information feature extraction and adaptive boost algorithm

A technology of error back propagation and self-adaptive enhancement, applied in the field of image processing, it can solve the problems of difficulty in finding and affecting accidents, and achieve the effect of high behavior detection accuracy, low computational complexity, and accurate behavior recognition.

Active Publication Date: 2016-10-12
BEIHANG UNIV
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Problems solved by technology

However, when an accident occurs, it is still difficult to detect the accident in time because the staff in the monitoring room are faced with numerous surveillance videos.
For example, if there is a sudden robbery in the mall, if it is not dealt with in time, it will have a greater impact

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  • Abnormal behavior identification method in error BP Adaboost network based on video motion information feature extraction and adaptive boost algorithm
  • Abnormal behavior identification method in error BP Adaboost network based on video motion information feature extraction and adaptive boost algorithm
  • Abnormal behavior identification method in error BP Adaboost network based on video motion information feature extraction and adaptive boost algorithm

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[0037] In order to make the purpose, technical solution and advantages of the present invention clearer, the present invention will be further described in detail below in conjunction with the accompanying drawings and specific embodiments.

[0038] The abnormal behavior recognition method based on video motion information feature extraction and BP Adaboost described in the present invention first decomposes the sample video into a single frame, calculates the optical flow from the adjacent frames of the video, and calculates the optical flow according to the optical flow in the horizontal direction and the vertical direction Direction, the optical flow direction histogram is calculated with the intensity of the optical flow as the weight, and then the histogram features are converted into feature vectors with probability attributes. According to the video samples of normal and abnormal classes, the BP Adaboost model is trained to obtain a high-accuracy classifier. When decomp...

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Abstract

The invention relates to an abnormal behavior identification method in an error BP Adaboost network based on video motion information feature extraction and an adaptive boost algorithm. The method comprises: light streams are calculated according to adjacent image frames of a video; a light stream direction is calculated by light streams at a horizontal direction and a vertical direction, a light stream direction histogram is calculated by using the intensity of the light stream as a weight, and then a histogram feature is converted into a feature attribute having a probability attribute; an error BP Adaboost network based on an adaptive boost algorithm is trained based on normal and abnormal training samples, thereby obtaining a classifier; at a testing stage, before the classification model obtained by training is used, a light stream direction histogram of a testing sample is obtained according to a calculation method with the same light stream histogram of the adjacent frames; and then abnormal behavior identification in the testing sample is carried out according to the classification model obtained by training and learning. According to the invention, the method has characteristics of high identification rate and low calculation complexity and can be widely applied to abnormal behavior identification and action analysis fields.

Description

technical field [0001] The invention relates to image processing technology, in particular to an abnormal behavior recognition method based on feature extraction of video motion information and error backpropagation network (BP Adaboost) based on adaptive enhancement algorithm. Background technique [0002] As we all know, in order to ensure the safety of public places, monitoring has been widely used. However, when an accident occurs, it is still difficult to detect the accident in time because the staff in the monitoring room are faced with numerous surveillance videos. For example, if there is a sudden robbery in a shopping mall, if it is not dealt with in time, it will have a greater impact. Therefore, public places such as shopping malls, busy streets, railway stations, and sports fields all need intelligent monitoring. [0003] Abnormal behavior recognition in videos has been widely concerned by scholars at home and abroad. Typically, we divide abnormal behavior int...

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Application Information

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IPC IPC(8): G06K9/00G06K9/62
CPCG06V40/20G06V20/42G06F18/214G06F18/24
Inventor 王田张雨琪乔美娜陶飞
Owner BEIHANG UNIV
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