Pig gait anomaly detection method based on ellipse fitting and predictive neural network

An ellipse fitting and neural network technology, applied in the field of pig gait anomaly detection based on ellipse fitting and predictive neural network, can solve problems such as the spread of the epidemic and the lack of detection and treatment of pig diseases.

Inactive Publication Date: 2017-09-05
JIANGSU POLYTECHNIC COLLEGE OF AGRI & FORESTRY
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Problems solved by technology

However, some pig diseases are still not detected and treated in a timely and effective manner
For pig diseases such as foot-and-mouth disease that can cause abno

Method used

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  • Pig gait anomaly detection method based on ellipse fitting and predictive neural network
  • Pig gait anomaly detection method based on ellipse fitting and predictive neural network
  • Pig gait anomaly detection method based on ellipse fitting and predictive neural network

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Embodiment Construction

[0029] The present invention will be described in detail below in conjunction with the accompanying drawings.

[0030] Such as figure 1 As shown, a pig gait abnormality detection method based on ellipse fitting and predictive neural network, including the following steps:

[0031] (1) Image acquisition and preprocessing

[0032] Image acquisition is carried out by the image acquisition system, and it is preprocessed. First of all, in order to obtain a clear outline of the pig, the target video is shot under ideal conditions such as the pig house environment and light. Then, continuous single-frame images are extracted from the target video, and the background subtraction method is used to detect the target image pig. Furthermore, binarization and morphological processing are used to obtain the complete target image. Finally, the canny operator is used to extract the target contour. The extraction process of the target contour is as follows: figure 2 shown.

[0033] (2)...

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Abstract

The invention discloses a pig gait anomaly detection method based on ellipse fitting and a predictive neural network. The method comprises steps: a video sample needed for an experiment is collected; the video sample is intercepted to acquire continuous target frames, the target frames are pre-processed, and a pig contour sequence with normal walking and abnormal gaits is obtained; ellipse fitting is used for modeling each part of the pig body, and a pig walking gait feature parameter sequence is built; through principal component analysis, the extracted features are subjected to optimized processing, and a feature sequence is extracted; and the predictive neural network is used for building a training model about normal walking and abnormal gait feature sequences, and through the training model, whether the inputted gait sequence belongs to abnormal walking is detected. Abnormal walking, such as laming, diseases of the forelimbs and instable forelimb walking caused by injuries of the pig, can be effectively recognized, and a good basis is provided for realizing a large-scale and intelligent pig industry.

Description

technical field [0001] The invention relates to image processing and pattern recognition, in particular to a pig gait abnormal detection method based on ellipse fitting and predictive neural network. Background technique [0002] At this stage, the gait detection and application for the human body have been greatly developed, and the angle information related to the gait when people walk has also become one of the important factors for identifying and judging the gait. In 2008, the research team of the College of Agricultural and Biosystems Engineering of Iowa State University in the United States studied the comfort evaluation and control of pigs in captivity based on machine vision, and carried out visual monitoring of the sleeping position of multiple pigs for temperature comfort , so as to evaluate the environment of the pig house and intelligently regulate the temperature of the pig house, in order to realize the intelligent pig breeding mode. In 2009, a research team ...

Claims

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Application Information

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IPC IPC(8): G06K9/00G06N3/08
CPCG06N3/08G06V40/25
Inventor 吴燕李娜崔明
Owner JIANGSU POLYTECHNIC COLLEGE OF AGRI & FORESTRY
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