Automated Tracking System for Identification of Tagged Mice for Automatic Social Behavior Analysis

Fabio Marcuccio; Alena Savonenko; Ralph Etienne-Cummings

doi:10.1109/BIOCAS.2018.8584712

Automated Tracking System for Identification of Tagged Mice for Automatic Social Behavior Analysis

Fabio Marcuccio, Alena Savonenko, Ralph Etienne-Cummings

School of Medicine

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Abstract

Monitoring mice social behaviors is extremely important for neurobehavioral analysis. State-of-the-art monitoring systems still require human handling for phenotype characterization with high cost and low standardization. Mice tracking and identity preservation represent the first step for phenotyping. This paper focuses on a new automated tracking system able to identify mice and keep their identities frame by frame, laying the groundwork for automatic social behavior analysis. Our system achieves more than 80% accuracy on metal ear tags identification on one-minute long videos recorded at 30 fps.

Original language	English (US)
Title of host publication	2018 IEEE Biomedical Circuits and Systems Conference, BioCAS 2018 - Proceedings
Publisher	Institute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)	9781538636039
DOIs	https://doi.org/10.1109/BIOCAS.2018.8584712
State	Published - Dec 20 2018
Event	2018 IEEE Biomedical Circuits and Systems Conference, BioCAS 2018 - Cleveland, United States Duration: Oct 17 2018 → Oct 19 2018

Other

Other	2018 IEEE Biomedical Circuits and Systems Conference, BioCAS 2018
Country/Territory	United States
City	Cleveland
Period	10/17/18 → 10/19/18

ASJC Scopus subject areas

Electrical and Electronic Engineering
Health Informatics
Instrumentation
Signal Processing
Biomedical Engineering

Access to Document

10.1109/BIOCAS.2018.8584712

Cite this

Automated Tracking System for Identification of Tagged Mice for Automatic Social Behavior Analysis. / Marcuccio, Fabio; Savonenko, Alena; Etienne-Cummings, Ralph.

2018 IEEE Biomedical Circuits and Systems Conference, BioCAS 2018 - Proceedings. Institute of Electrical and Electronics Engineers Inc., 2018. 8584712.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Marcuccio, F, Savonenko, A & Etienne-Cummings, R 2018, Automated Tracking System for Identification of Tagged Mice for Automatic Social Behavior Analysis. in 2018 IEEE Biomedical Circuits and Systems Conference, BioCAS 2018 - Proceedings., 8584712, Institute of Electrical and Electronics Engineers Inc., 2018 IEEE Biomedical Circuits and Systems Conference, BioCAS 2018, Cleveland, United States, 10/17/18. https://doi.org/10.1109/BIOCAS.2018.8584712

@inproceedings{0356940a351449acacf2db34d96346fb,

title = "Automated Tracking System for Identification of Tagged Mice for Automatic Social Behavior Analysis",

abstract = "Monitoring mice social behaviors is extremely important for neurobehavioral analysis. State-of-the-art monitoring systems still require human handling for phenotype characterization with high cost and low standardization. Mice tracking and identity preservation represent the first step for phenotyping. This paper focuses on a new automated tracking system able to identify mice and keep their identities frame by frame, laying the groundwork for automatic social behavior analysis. Our system achieves more than 80% accuracy on metal ear tags identification on one-minute long videos recorded at 30 fps.",

author = "Fabio Marcuccio and Alena Savonenko and Ralph Etienne-Cummings",

year = "2018",

month = dec,

day = "20",

doi = "10.1109/BIOCAS.2018.8584712",

language = "English (US)",

booktitle = "2018 IEEE Biomedical Circuits and Systems Conference, BioCAS 2018 - Proceedings",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

note = "2018 IEEE Biomedical Circuits and Systems Conference, BioCAS 2018 ; Conference date: 17-10-2018 Through 19-10-2018",

}

TY - GEN

T1 - Automated Tracking System for Identification of Tagged Mice for Automatic Social Behavior Analysis

AU - Marcuccio, Fabio

AU - Savonenko, Alena

AU - Etienne-Cummings, Ralph

PY - 2018/12/20

Y1 - 2018/12/20

N2 - Monitoring mice social behaviors is extremely important for neurobehavioral analysis. State-of-the-art monitoring systems still require human handling for phenotype characterization with high cost and low standardization. Mice tracking and identity preservation represent the first step for phenotyping. This paper focuses on a new automated tracking system able to identify mice and keep their identities frame by frame, laying the groundwork for automatic social behavior analysis. Our system achieves more than 80% accuracy on metal ear tags identification on one-minute long videos recorded at 30 fps.

AB - Monitoring mice social behaviors is extremely important for neurobehavioral analysis. State-of-the-art monitoring systems still require human handling for phenotype characterization with high cost and low standardization. Mice tracking and identity preservation represent the first step for phenotyping. This paper focuses on a new automated tracking system able to identify mice and keep their identities frame by frame, laying the groundwork for automatic social behavior analysis. Our system achieves more than 80% accuracy on metal ear tags identification on one-minute long videos recorded at 30 fps.

UR - http://www.scopus.com/inward/record.url?scp=85060863539&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85060863539&partnerID=8YFLogxK

U2 - 10.1109/BIOCAS.2018.8584712

DO - 10.1109/BIOCAS.2018.8584712

M3 - Conference contribution

AN - SCOPUS:85060863539

BT - 2018 IEEE Biomedical Circuits and Systems Conference, BioCAS 2018 - Proceedings

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 2018 IEEE Biomedical Circuits and Systems Conference, BioCAS 2018

Y2 - 17 October 2018 through 19 October 2018

ER -

Automated Tracking System for Identification of Tagged Mice for Automatic Social Behavior Analysis

Abstract

Other

ASJC Scopus subject areas

Access to Document

Other files and links

Fingerprint

Cite this