Animal Pose Estimation
22 papers with code • 6 benchmarks • 16 datasets
Animal pose estimation is the task of identifying the pose of an animal.
( Image credit: Using DeepLabCut for 3D markerless pose estimation across species and behaviors )
Benchmarks
These leaderboards are used to track progress in Animal Pose Estimation
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Libraries
Use these libraries to find Animal Pose Estimation models and implementationsDatasets
Animal-Pose Dataset
AP-10K
StanfordExtra
Animal Kingdom
Horse-10
AwA Pose
ATRW
Marmoset-8K
TriMouse-161
Fish-100
Most implemented papers
AP-10K: A Benchmark for Animal Pose Estimation in the Wild
The experimental results provide sound empirical evidence on the superiority of learning from diverse animals species in terms of both accuracy and generalization ability.
APT-36K: A Large-scale Benchmark for Animal Pose Estimation and Tracking
Based on APT-36K, we benchmark several representative models on the following three tracks: (1) supervised animal pose estimation on a single frame under intra- and inter-domain transfer learning settings, (2) inter-species domain generalization test for unseen animals, and (3) animal pose estimation with animal tracking.
SuperAnimal pretrained pose estimation models for behavioral analysis
Quantification of behavior is critical in applications ranging from neuroscience, veterinary medicine and animal conservation efforts.
Multi-animal pose estimation, identification and tracking with DeepLabCut
Estimating the pose of multiple animals is a challenging computer vision problem: frequent interactions cause occlusions and complicate the association of detected keypoints to the correct individuals, as well as having highly similar looking animals that interact more closely than in typical multi-human scenarios.
11K Hands: Gender recognition and biometric identification using a large dataset of hand images
In this work, we propose a large dataset of human hand images (dorsal and palmar sides) with detailed ground-truth information for gender recognition and biometric identification.
Markerless tracking of user-defined features with deep learning
Quantifying behavior is crucial for many applications in neuroscience.
Pretraining boosts out-of-domain robustness for pose estimation
Neural networks are highly effective tools for pose estimation.
WormPose: Image synthesis and convolutional networks for pose estimation in C. elegans
An important model system for understanding genes, neurons and behavior, the nematode worm C. elegans naturally moves through a variety of complex postures, for which estimation from video data is challenging.
ImageNet performance correlates with pose estimation robustness and generalization on out-of-domain data
DeepLabCut/DeepLabCut
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ICML UDL 2020
Neural networks are highly effective tools for pose estimation.
Structured Context Enhancement Network for Mouse Pose Estimation
However, quantifying mouse behaviours from videos or images remains a challenging problem, where pose estimation plays an important role in describing mouse behaviours.
