Real-time recognition of cattle using animal biometrics

With the advent of efficient recognition techniques, animal biometric systems have gained more proliferation for the identification and monitoring of cattle. A cattle biometric system is a pattern recognition-based system for the identification of livestock. In this paper, we propose a novel muzzle...

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Vydáno v:Journal of real-time image processing Ročník 13; číslo 3; s. 505 - 526
Hlavní autoři: Kumar, Santosh, Singh, Sanjay Kumar, Singh, Ravi Shankar, Singh, Amit Kumar, Tiwari, Shrikant
Médium: Journal Article
Jazyk:angličtina
Vydáno: Berlin/Heidelberg Springer Berlin Heidelberg 01.09.2017
Springer Nature B.V
Témata:
Algorithms
Animals
Biometrics
Cameras
Cattle
Computer Graphics
Computer Science
Forgery
Fraud
Identification
Image databases
Image Processing and Computer Vision
Insurance claims
Livestock
Multimedia Information Systems
Pattern Recognition
Public domain
Real time
Registration
Semiconductors
Signal,Image and Speech Processing
Special Issue Paper
Surveillance
Wireless networks
Real-time image processing
FLDA
FLPP
Classification
Cattle recognition
Feature extraction
SVM
Animal biometrics
Muzzle point
ISSN:1861-8200, 1861-8219
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Shrnutí:With the advent of efficient recognition techniques, animal biometric systems have gained more proliferation for the identification and monitoring of cattle. A cattle biometric system is a pattern recognition-based system for the identification of livestock. In this paper, we propose a novel muzzle point recognition based on Fisher locality preserving projection algorithm for the recognition of cattle in real time. We have captured images of animals using a surveillance camera and transferred them to the server by wireless network technology. The major contributions are as follows: (1) preparation of muzzle point database, (2) extraction of the salient set of features using proposed muzzle point recognition approach, and (3) evaluation and comparison analysis of the introduced method and several existing recognition algorithms on a standard benchmark protocol. The efficacy of proposed muzzle point recognition approach for cattle evaluates under identification settings and yields 96.87 % recognition accuracy for identifying individual cattle. The proposed approach also valued the 10.25 sec recognition time for enrollment and identified individual cattle on different sizes of muzzle point images.
Bibliografie:ObjectType-Article-1
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ISSN:1861-8200
1861-8219
DOI:10.1007/s11554-016-0645-4