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 | |
| 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 | United States |
| City | Cleveland |
| Period | 10/17/18 → 10/19/18 |
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ASJC Scopus subject areas
- Electrical and Electronic Engineering
- Health Informatics
- Instrumentation
- Signal Processing
- Biomedical Engineering
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
}
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.
ER -