Deer Body Adaptive Threshold Segmentation Algorithm Based on Color Space

In large-scale deer farming image analysis,K-means or maximum between-class variance(Otsu)algorithms can be used to distinguish the deer from the background.However,in an actual breeding environment,the barbed wire or chain-link fencing has a certain isolating effect on the deer which greatly interferes with the identification of the individual deer.Also,when the target and background grey values are similar,the multiple background targets cannot be completely separated.To better identify the posture and behaviour of deer in a deer shed,we used digital image processing to separate the deer from the background.To address the problems mentioned above,this paper proposes an adaptive threshold segmentation algorithm based on color space.First,the original image is pre-processed and optimized.On this basis,the data are enhanced and contrasted.Next,color space is used to extract the several backgrounds through various color channels,then the adaptive space segmentation of the extracted part of the color space is performed.Based on the segmentation effect of the traditional Otsu algorithm,we designed a comparative experiment that divided the four postures of turning,getting up,lying,and standing,and successfully separated multiple target deer from the background.Experimental results show that compared with K-means,Otsu and hue saturation value(HSV)+K-means,this method is better in performance and accuracy for adaptive segmentation of deer in artificial breeding scenes and can be used to separate artificially cultivated deer from their backgrounds.Both the subjective and objective aspects achieved good segmentation results.This article lays a foundation for the effective identification of abnormal behaviour in sika deer.

A Novel Hybrid Tag Identification Protocol for Large-Scale RFID Systems

Radio frequency identification technology is one of the main technologies of Internet of Things(IoT).Through the transmission and reflection of wireless radio frequency signals,non-contact identification is realized,and multiple objects identification can be realized.However,when multiple tags communicate with a singleton reader simultaneously,collision will occur between the signals,which hinders the successful transmissions.To effectively avoid the tag collision problem and improve the reading performance of RFID systems,two advanced tag identification algorithms namely Adaptive M-ary tree slotted Aloha(AMTS)based on the characteristics of Aloha-based and Query tree-based algorithms are proposed.In AMTS,the reader firstly uses the framed slotted Aloha protocol to map the tag set to different time slots,and then identify the collided tags using binary search method based on collision factor or mapping table.Both performance analysis and extensive experimental results indicate that our proposed algorithms significantly outperforms most existing anti-collision approaches in tag dense RFID systems.