Intelligent pig captive breeding monitoring system based on video activity analysis

Abstract

The invention discloses an intelligent pig captive breeding monitoring system based on video activity analysis, and aims to solve the problem that monitoring of the instance level of animal behaviorsin an animal husbandry breeding captive breeding scene is still to be improved. The method comprises: using semantic segmentation based on a deep convolutional neural network for obtaining an animal mask; segmenting the colored ear tag of the animal by using a color saliency formula; processing the mask for semantic segmentation through a distance transformation method to obtain an instance segmentation result; associating the ear tag with the instance by using a distance-first method and an attitude constraint condition according to an instance segmentation result to obtain an animal skeletonmodel; based on the animal skeleton model, obtaining the attributes of the center point position and the head orientation, and adding the attributes into a region-of-interest counter; and obtaining atail biting judgment method of the pig based on the interested counter. According to the method, the monitoring video information is utilized, the ear tag information is combined, and the time pointneeding to be noticed in the video is marked through the region-of-interest event based on the skeleton model.

Description

technical field [0001] The invention relates to a computer vision monitoring and analysis system in the scene of animal breeding and captive breeding, in particular to an intelligent pig captive breeding monitoring system based on video activity analysis. Background technique [0002] Animal husbandry monitoring is an industry that responds to the concept of big data and artificial intelligence. In recent years, many sheds for captive animals have deployed video surveillance, but its role is limited to security and manual observation of animal habits. The task characteristics of observing the special behavior of animals determine that the work efficiency of manual observation of animal habits is low. On the one hand, manpower and time are wasted, and on the other hand, there is the possibility of missed detection. These factors further reduce the actual value of video surveillance. The reason for the above-mentioned contradiction is that the video sequence is not introduced ...

AI Technical Summary

Problems solved by technology

[0005] The present invention overcomes the problem in the prior art that the instance-level monitoring of animal behavior in the animal husbandry breeding scene still needs to be improved, and provides an intelligent system based on video activity analysis that can assist manual playback of video and provide reference data for computer analysis. Pig Breeding Monitoring System

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