AP-10K is the first large-scale benchmark for general animal pose estimation, to facilitate the research in animal pose estimation. AP-10K consists of 10,015 images collected and filtered from 23 animal families and 60 species following the taxonomic rank and high-quality keypoint annotations labeled and checked manually.
24 PAPERS • 1 BENCHMARK
Animal Kingdom is a large and diverse dataset that provides multiple annotated tasks to enable a more thorough understanding of natural animal behaviors. The wild animal footage used in the dataset records different times of the day in an extensive range of environments containing variations in backgrounds, viewpoints, illumination and weather conditions. More specifically, the dataset contains 50 hours of annotated videos to localize relevant animal behavior segments in long videos for the video grounding task, 30K video sequences for the fine-grained multi-label action recognition task, and 33K frames for the pose estimation task, which correspond to a diverse range of animals with 850 species across 6 major animal classes.
14 PAPERS • 2 BENCHMARKS
The ATRW Dataset contains over 8,000 video clips from 92 Amur tigers, with bounding box, pose keypoint, and tiger identity annotations.
6 PAPERS • NO BENCHMARKS YET
Horse-10 is an animal pose estimation dataset. It comprises 30 diverse Thoroughbred horses, for which 22 body parts were labeled by an expert in 8,114 frames (animal pose estimation). Horses have various coat colors and the “in-the-wild” aspect of the collected data at various Thoroughbred yearling sales and farms added additional complexity. The authors introduce Horse-C to contrast the domain shift inherent in the Horse-10 dataset with domain shift induced by common image corruptions.
5 PAPERS • 1 BENCHMARK
AwA Pose is a large scale animal keypoint dataset with ground truth annotations for keypoint detection of quadruped animals from images.
4 PAPERS • NO BENCHMARKS YET
Schools of inland silversides (Menidia beryllina, n=14 individuals per school) were recorded in the Lauder Lab at Harvard University while swimming at 15 speeds (0.5 to 8 BL/s, body length, at 0.5 BL/s intervals) in a flow tank with a total working section of 28 x 28 x 40 cm as described in previous work, at a constant temperature (18±1°C) and salinity (33 ppt), at a Reynolds number of approximately 10,000 (based on BL). Dorsal views of steady swimming across these speeds were recorded by high-speed video cameras (FASTCAM Mini AX50, Photron USA, San Diego, CA, USA) at 60-125 frames per second (feeding videos at 60 fps, swimming alone 125 fps). The dorsal view was recorded above the swim tunnel and a floating Plexiglas panel at the water surface prevented surface ripples from interfering with dorsal view videos. Five keypoints were labeled (tip, gill, peduncle, dorsal fin tip, caudal tip). 100 frames were labeled, making this a real-world sized laboratory dataset.
2 PAPERS • 1 BENCHMARK
Dataset page: https://github.com/mosamdabhi/MBW-Data
2 PAPERS • NO BENCHMARKS YET
The dataset is designed specifically to solve a range of computer vision problems (2D-3D tracking, posture) faced by biologists while designing behavior studies with animals.
1 PAPER • NO BENCHMARKS YET
MacaquePose is an animal pose estimation dataset containing pictures of macaque monkeys and manually labeled annotations on them.
1 PAPER • 1 BENCHMARK
Vinegar Fly is a pose estimation dataset for fruit flies.