Search

The basic behavioral characteristics of live pigs are mainly shown through daily food intake frequency, water intake frequency, and excretion frequency. These factors indicate the health states of pig growth. Monitoring and analyzing the behavioral characteristics of pigs are important basis to understand their health situations. Currently, we mainly use artificial way to monitor livestock behavior in China. This method consumes large amounts of human labor and energy, and the observed data obtained in this way is subjective. It is difficult to ensure the accuracy and the continuity of the records. We take good advantage of pig detection and tracking technology based on machine vision to monitor the behavior of pigs to evaluate the health status of pigs in time, and to reduce the morbidity and mortality of pigs and increase the slaughtering rate of pigs. It has important practical significance and application value in improving people’s confidence in pork quality and increasing the income of farmers. Target tracking technology is the basis of the moving target identification and abnormal behavior tracking, recording and analysis. We research the real-time monitoring of the target pigs foraging based on the particle filter target tracking technology. Particle filter algorithm closely approximates Bayesian filtering algorithm based on Monte Carlo simulation, and it is used in target tracking widely. Conceptually, a particle filter tracker maintains a probability distribution over the state (location, scale, and so on) of the object being tracked. Particle filters represent this distribution as a set of weighted samples, or particles. Each particle represents a possible instantiation of the state of the object. In other words, each particle is a guess representing one possible location of the object being tracked. The set of particles contain more weight at locations where the object being tracked is more likely to be. This weighted distribution is propagated through time using a set of equations known as the Bayesian filtering equations, and we can determine the trajectory of the tracked object by the particle with the highest weight or the weighted mean of the particle set at each time step. In view of the pig behavior characteristics and the actual situation of the farms' video image acquisition, this paper takes a group of pigs raised as detection tracking target. On the basis of analyzing and summarizing in particle filter tracking algorithm, we carried out particle filter target tracking technology for pigs which is based on the color characteristics to achieve the goal of tracking pigs. In order to solve the problems in the color characteristics of particle filter target tracking for pigs, we fused the color characteristics and the target contour centroid feature. The specific methods were as follows: First of all, according to the particle filter tracking algorithm based on single color feature of target tracking on the position of the rectangle coordinates, and the height and width of the target tracking rectangular box, we calculated the center of the target tracking rectangle coordinates. Secondly, we determined the centroid position of moving pigs on the basis of the comparison and analysis of moving target centroid position and the minimum circumscribed rectangle length-width ratio. Finally, according to the target contour centroid location and the center of the tracking target rectangle coordinates, we calculated the amount of deviation between them. When the deviation of target contour centroid and tracking rectangular box was too large, we took a second correction for tracking the target coordinates based on the particle filter algorithm with multi-feature fusion. The improved algorithm presented in this paper updated the tracking rectangular coordinates through the target contour centroid coordinates, and gave the new tracking rectangular box. This paper constructs the target pig tracking system based on particle filter algorithm, achieves a multi-feature fusion particle filter tracking algorithm through area real-time monitoring, and completes the statistics of the target pig's feeding time and food intake frequency. Experiment results prove that this algorithm can automatically accurately track, record and analyze the feeding behaviour of the target pigs, and effectively deal with the problems such as target short-time missing. The feeding frequency and time of the target pigs are almost the same as the manual statistics. [ABSTRACT FROM AUTHOR]

The breeding environment plays an important role in healthy growth and development of the pigs, and it is also a guarantee of the excellent traits. The growth of pig is the integrated result of their own traits and external environmental factors. For different environments, pigs will show different behaviors. It is found that the appropriate growth environment can be reflected by their degree of dispersion in the pens. When the environment is suitable, the pig group will lie down together. When the temperature is low, they will huddle together; and when the environmental temperature is high, the pigs will scatter around. Image segmentation is the important link to analyze pig behavior status based on computer vision technology, and pig image segmentation is an image representation that provides easy-to-understand and analysis for the behavior feature extraction, parameter measurement, image analysis, pattern recognition of individual pig. The accurate and effective image segmentation algorithm is for pig behavioral intelligent analysis, understanding and environmental intelligent decision. The traditional Graph Cut algorithm uses the energy function to compute all the pixels in the image, it takes a lot of time. It has the low segmentation efficiency and it cannot accurately segment specific pig. Aiming at the limitation of traditional Graph Cut algorithm and the characteristic of image itself, on the basis of the interactive watershed algorithm, in this paper, we proposed an interactive image segmentation method based on improved Graph Cut algorithm aiming at better use of interaction information provided by the user and effectively control of the number of super pixels. To some extent, the algorithm could also avoid the problems of over segmentation or under segmentation. The image was divided into regions based on the interactive watershed algorithm, and the foreground and background were specified. Each region block was regarded as the super pixel, the gray scale of super pixel was used as the vertex. Establish edges between adjacent vertices and the new network map was constructed instead of the traditional weighted graph. In order to reconstruct the energy function to complete the effective segmentation of the foreground and background, the maximum flow, and minimization cut algorithm were used to cut the image. Results of experiments showed that the method can effectively segment pig target, it had high segmentation quality and high efficiency. In this paper, the peak signal to noise ratio (PSNR) and structural similarity (SSIM) were used as objective evaluation standard. The range of the PSNR was [30, 40] and the range of the SSIM was [0.9, 1] in this method, which was the most accurate segmentation algorithm in the shown segmentation algorithm. It indicated that two evaluation criterions were consistent with the subjective evaluation, and the stability and the reliability of this method were improved. The average time consumed in this algorithm was only 33.7% of in the traditional Graph Cut algorithm. It greatly improved the efficiency of segmentation, and it had highly robust for the segmentation of specific target pigs. The results of this paper can provide technical support for subsequent target identification, detection, tracking and monitoring, evaluation of external environments, such as alarms. [ABSTRACT FROM AUTHOR]