A benchmark database for animal re-identification and tracking

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While there are multiple sources of annotated images and videos for human and vehicle re-identification, databases for individual animal recognition are still in demand. We present a database containing five annotated video clips each containing between 9 and 27 identities. The overall number of individual animals is 20,490, and the total number of classes is 93. The database can be used for testing novel methods for animal reidentification, object detection and tracking. The main challenge of the database is that multiple animals are present in the same video frame, leading to problems with occlusion and noisy, cluttered bounding boxes. To set-up a benchmark on individual animal recognition, we trained and tested 26 classification methods for the five videos and three feature representations. We also report results with state-of-the-art deep learning methods for object detection (MMDet) and tracking (Uni-Track).


  • Animal re-identification, Benchmark database, Classification of images, Object detection and tracking
Original languageEnglish
Title of host publicationProc. of the Fifth IEEE International Conference on Image Processing, Applications and Systems (IPAS)
Publication statusPublished - 6 Dec 2023
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