Accepted author manuscript, 889 KB, PDF document
Available under license: CC BY: Creative Commons Attribution 4.0 International License
Final published version
Licence: CC BY: Creative Commons Attribution 4.0 International License
Research output: Contribution to Journal/Magazine › Journal article › peer-review
Research output: Contribution to Journal/Magazine › Journal article › peer-review
}
TY - JOUR
T1 - Social Behavioral Phenotyping of Drosophila with a 2D-3D Hybrid CNN Framework
AU - Jiang, Ziping
AU - Chazot, Paul L
AU - Celebi, M. Emre
AU - Crookes, Danny
AU - Jiang, Richard
PY - 2019/5/15
Y1 - 2019/5/15
N2 - Behavioural phenotyping of drosphila is an important means in biological and medical research to identify genetic, pathologic or psychologic impact on animal behviour. Automated behavioural phenotyping from videos has been a desired capability that can waive long-time boring manual work in behavioral analysis. In this paper, we introduced deep learning into this challenging topic, and proposed a new 2D+3D hybrid CNN framework for drosphila’s social behavioural phenotyping. In the proposed multitask learning framework, action detection and localization of drosphila jointly is carried out with action classification, and a given video is divided into clips with fixed length. Each clip is fed into the system and a 2-D CNN is applied to extract features at frame level. Features extracted from adjacent frames are then connected and fed into a 3-D CNN with a spatial region proposal layer for classification. In such a 2D+3D hybrid framework, drosophila detection at the frame level enables the action analysis at different durations instead of a fixed period. We tested our framework with different base layers and classification architectures and validated the proposed 3D CNN based social behavioral phenotyping framework under various models, detectors and classifiers.
AB - Behavioural phenotyping of drosphila is an important means in biological and medical research to identify genetic, pathologic or psychologic impact on animal behviour. Automated behavioural phenotyping from videos has been a desired capability that can waive long-time boring manual work in behavioral analysis. In this paper, we introduced deep learning into this challenging topic, and proposed a new 2D+3D hybrid CNN framework for drosphila’s social behavioural phenotyping. In the proposed multitask learning framework, action detection and localization of drosphila jointly is carried out with action classification, and a given video is divided into clips with fixed length. Each clip is fed into the system and a 2-D CNN is applied to extract features at frame level. Features extracted from adjacent frames are then connected and fed into a 3-D CNN with a spatial region proposal layer for classification. In such a 2D+3D hybrid framework, drosophila detection at the frame level enables the action analysis at different durations instead of a fixed period. We tested our framework with different base layers and classification architectures and validated the proposed 3D CNN based social behavioral phenotyping framework under various models, detectors and classifiers.
U2 - 10.1109/ACCESS.2019.2917000
DO - 10.1109/ACCESS.2019.2917000
M3 - Journal article
VL - 7
SP - 67972
EP - 67982
JO - IEEE Access
JF - IEEE Access
SN - 2169-3536
ER -