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Human body abnormal behavior detection and recognition system and method

A recognition method and recognition system technology, applied in the field of surveillance cameras, can solve the problems of wasting particles, not considering data, etc., and achieve the effects of improving accuracy, good speed and robustness, and improving tracking performance

Active Publication Date: 2019-10-18
PINGDINGSHAN UNIVERSITY
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AI Technical Summary

Problems solved by technology

Studies have shown that the combination of particle filter and CAMS can improve the performance of online tracking, but the particle filter uses the transition priority as the proposal distribution and does not consider the currently observed data, resulting in the waste of many particles in low probability regions

Method used

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  • Human body abnormal behavior detection and recognition system and method
  • Human body abnormal behavior detection and recognition system and method
  • Human body abnormal behavior detection and recognition system and method

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Embodiment Construction

[0046] The technical solutions of the present invention will be further described in detail below in conjunction with the accompanying drawings and specific embodiments.

[0047] 1 Human Abnormal Behavior Detection and Recognition Method Based on Hybrid Algorithm

[0048] The framework of the proposed mixed algorithm human abnormal behavior detection and recognition system is as follows: figure 1 shown. First, a sequence of video frames is passed to a color conversion module to decompose the color of the target object in the frames. If there are other objects with similar colors in the background of the target object, the CBWH module is used to restore the screening of the target object from its background interference, and determine the possible color distribution, and then call the CAMS module; otherwise, directly call the CAMS module. In addition, when the path of the target object is blocked by obstacles, the CAMS module will output to the UPF module to correctly estimat...

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Abstract

The invention discloses a human body abnormal behavior detection and recognition system and method, and belongs to the technical field of monitoring camera shooting. The mixing method is based on continuous adaptive mean shift (CAMS). A correction background weight histogram CBWH and an unscented particle filter UPF technology are introduced to process interference of shielded and similar color objects. A detection mode based on sparse representation is adopted to detect and identify abnormal behaviors of a target object from multiple scenes, the performance of the proposed method is evaluatedby using mean square error statistics, and simulation verification is carried out on a public data set UMN at the same time. Experimental results show that the method provided by the invention can accurately detect and identify the target object under the condition that obstacles are shielded or other objects with similar colors exist in different scenes. In addition, the technology may further improve the tracking performance of the target object in multiple cameras in a complex scene.

Description

technical field [0001] The invention belongs to the technical field of surveillance cameras, and relates to a human body abnormal behavior detection and recognition system and method, in particular to a human body abnormal behavior detection and recognition system and method based on a hybrid algorithm under indoor video surveillance. Background technique [0002] In recent years, surveillance cameras have been widely used in banks, supermarkets, prisons, airports, parking lots, gas stations, rescue, medical testing and other scenes to ensure the safety of people's lives and property and social stability. At the same time, it is also widely used in the detection and recognition of abnormal behaviors of the elderly indoors. However, due to the interference of factors such as indoor lighting changes, occlusion, and other objects in similar backgrounds, the detection and recognition of abnormal behaviors is challenging. A lot of research work has been carried out on the improve...

Claims

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Application Information

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IPC IPC(8): G06T7/20G06T7/277G06K9/00
CPCG06T7/20G06T7/277G06T2207/10016G06T2207/30232G06V40/20G06V20/52
Inventor 刘建芳郑浩夏栋梁廖梦怡邢立国史玉珍黄淼刘小满李成建刘继童
Owner PINGDINGSHAN UNIVERSITY
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