Animal Re-Identification from Video

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:

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