System and method for analyzing object tracking and object behavior using deep learning algorithms

摘要

A smart livestock management system using deep learning-based object tracking and behavior analysis according to another embodiment of the present invention is attached to the head of a pig object in a pig house, the EEG detection module for measuring and transmitting the EEG signal of the pig object; a first imaging device for photographing a motion image of a pig object in the pig house; a second imaging device generating a thermal image of the pig object; a sound collecting device for collecting sound information including breathing and coughing sounds of the pig object; a monitoring server for predicting and judging the presence or absence of disease and the type of disease in the pig object based on the EEG signal, the motion image, the thermal image and the sound information; and a manager terminal receiving a disease outbreak notification message of a pig object from the monitoring server, wherein the monitoring server is a pig object through the EEG variability of the EEG signal detected in the pig object. After classifying the related EEG (electroencephalographic, EEG), the EEG analysis unit to analyze the pattern of EEG variability in comparison with the reference; When a target object (pig) is input from the manager terminal, a path tracking unit for tracking the movement path of the target object in the reverse order based on the position signal of the EEG detection module located in the target object (pig); a behavior pattern and posture analysis unit that analyzes the behavior pattern and posture of the pig object based on a motion image for a change in movement of the targeted pig object; a body temperature analyzer analyzing the body temperature of the pig object based on the thermal image; an acoustic analysis unit for analyzing at least one of a breathing cycle of the pig object, a size and generation cycle of a cough sound, and a cry sound based on the acoustic information; And the presence or absence of disease is first determined based on the EEG variability pattern of the pig object, irregular postural changes of behavioral patterns, breathing cycle, and cough sound, and whether or not disease occurs based on the body temperature and body temperature change of each part of the pig object selected first and a disease occurrence determination unit for secondary determination, wherein the behavioral posture and pattern analysis unit classifies the change in the pose pattern of the pig in the pig house into any one of Standing, Lying, and Sitting based on the motion image, and when the pig moves E, the pose pattern change is analyzed as distance versus time, the body temperature analysis unit analyzes the body temperature change for each part of pigs that are heated according to the pose pattern change, and the disease occurrence determination unit learns the deep learning algorithm when a disease occurs It is characterized in that the pig object is first selected based on the change of the time-series pose pattern, breathing cycle, and cough sound of the pigs.