Repository of annotated videos, images and extracted features of multiple animals 1. Videos The videos are available in the file "videos.zip". The original videos included in this repository have been sourced from Pixabay under Pixabay License Free for commercial use No attribution required The video data is summarised below: Short Name Video Name # Frames Size # Bounding boxes # Identities Pigs Pigs_49651_960_540_500f.mp4 500 ( 960, 540) 6184 26 Koi fish Koi_5652_952_540.mp4 536 ( 952, 540) 1635 9 Pigeons (curb) Pigeons_8234_1280_720.mp4 443 (1280, 720) 4700 16 Pigeons (ground) Pigeons_4927_960_540_600f.mp4 600 ( 960, 540) 3079 17 Pigeons (square) Pigeons_29033_960_540_300f.mp4 300 ( 960, 540) 4892 28 2. Annotated videos The annotated videos are available in the file "annotated_videos.zip": Annotated_Pigs_49651_960_540_500f.mp4. Annotation contributed by Lucy Kuncheva Annotated_Koi_5652_952_540.mp4. Annotation contributed by Lucy Kuncheva Annotated_Pigeons_8234_1270_720.mp4. Annotation contributed by Wilf Langdon Annotated_Pigeons_4927_960_540_600f.mp4. Annotation contributed by Frank Krzyzowski Annotated_Pigeons_29033_960_540_300f.mp4. Annotation contributed by Owen West 3. Images The individual images are in the file "images.zip". For each video, all the images are in the corresponding folder. Inside, there is a folder for each individual with all the images. The filename of each image includes the frame number. 4. Frames information The correspondence between images and frames in the videos are in the file "frames.zip" The prefixes "h1_" and "h2_" denote, respectively, the first and second halves of the videos. The columns on these files are: x, y: coordinates in pixels of the top left corner of the bounding box. width, height: of the bounding box in pixels. frame: frame number. max_w, max_h. label: the label (class) number. image: file name. 5. Extracted features Files with the extracted features are in "features.zip". The prefixes "h1_" and "h2_" denote, respectively, the data corresponding to the first and second halves of the videos. Five representations are used: "RGB" moments. "HOG": Histogram of Oriented Gradients "LBP": Local Binary Patterns. "AE": AutoEncoders. "MN2": extracted from a Keras MobileNetV2 model pre-trained on Imagenet The representation appears as a postfix in the file names. In each csv file, each image appears as a row. The feature values followed by the label (class) number. 6. Source code Sample code (matlab & python) is available at https://github.com/admirable-ubu/animal-recognition

This work is supported by the UKRI Centre for Doctoral Training in Artificial Intelligence, Machine Learning and Advanced Computing (AIMLAC), funded by grant EP/S023992/1. This work is also supported by the Junta de Castilla y León under project BU055P20 (JCyL/FEDER, UE), and the Ministry of Science and Innovation under project PID2020-119894GB-I00 co-financed through European Union FEDER funds. J.L. Garrido-Labrador is supported through Consejería de Educaci\'on of the Junta de Castilla y León` and the European Social Fund through a pre-doctoral grant (EDU/875/2021). I. Ramos-Perez is supported by the predoctoral grant (BDNS 510149) awarded by the Universidad de Burgos, Spain. J.J. Rodríguez was supported by mobility grant PRX21/00638 of the Spanish Ministry of Universities.