Analysis method for recognizing attack behaviors of group-housed pigs through employing machine vision technology

Abstract

The invention discloses an analysis method for recognizing attack behaviors of group-housed pigs through employing the machine vision technology. The method comprises the steps: extracting an attack key frame sequence from a downward view group-housed pig video, and locating an attacking pig; taking the attacking pig as the whole body for the extraction of the acceleration characteristics; carrying out the training of the acceleration data, obtaining an acceleration threshold value, and dividing the key frame into a high-level frame, a middle-level frame and a non-attack frame according to thethreshold value; finally setting a minimum unit of the attack recognition, and classifying the group-housed pigs into high-level, middle-level and non-attack group-housed pigs according to the proportion of the attack frame in the unit. The method is used for the recognition of attack behaviors of the group-housed pigs through the machine vision technology, does not causes any impact on the group-housed pigs, provides a theoretical basis for the exploration of the attack rule, the evaluation of the damage level and the determination of manual intervention, and also provides reference for thedetection of abnormal behaviors of other livestock based on the accelerated movement.

Description

technical field [0001] The invention relates to technologies such as machine vision, pattern recognition, and animal behavior analysis, and in particular to a method for analyzing the attack behavior of group-raised pigs in a surveillance video under a bird's-eye view state. Background technique [0002] Because group-housed pigs face limited spatial capacity, barren environments, low-fiber diets, and repeatedly changing group composition in intensive agriculture, they exhibit higher levels of aggression than in natural environments. Aggression between pigs can result in trauma and infection of the skin, and even fatal injuries. Injured pigs have less access to food and thus have lower growth rates, affecting pork production. Attacks are considered to be one of the most important health, welfare and economic issues in modern production systems. At present, group pig attack identification mainly relies on manual observation and surveillance video. These methods are time-con...

AI Technical Summary

Problems solved by technology

At present, group pig attack identification mainly relies on manual observation and surveillance video. These methods are time-consuming, laborious and lagging, and it is difficult to realize real-time detection of attacks in large-scale farms.

Images