[2603.25524] CHIRP dataset: towards long-term, individual-level, behavioral monitoring of bird populations in the wild

Computer Science > Computer Vision and Pattern Recognition arXiv:2603.25524 (cs) [Submitted on 26 Mar 2026] Title:CHIRP dataset: towards long-term, individual-level, behavioral monitoring of bird populations in the wild Authors:Alex Hoi Hang Chan, Neha Singhal, Onur Kocahan, Andrea Meltzer, Saverio Lubrano, Miyako H. Warrington, Michel Griesser, Fumihiro Kano, Hemal Naik View a PDF of the paper titled CHIRP dataset: towards long-term, individual-level, behavioral monitoring of bird populations in the wild, by Alex Hoi Hang Chan and 8 other authors View PDF HTML (experimental) Abstract:Long-term behavioral monitoring of individual animals is crucial for studying behavioral changes that occur over different time scales, especially for conservation and evolutionary biology. Computer vision methods have proven to benefit biodiversity monitoring, but automated behavior monitoring in wild populations remains challenging. This stems from the lack of datasets that cover a range of computer vision tasks necessary to extract biologically meaningful measurements of individual animals. Here, we introduce such a dataset (CHIRP) with a new method (CORVID) for individual re-identification of wild birds. The CHIRP (Combining beHaviour, Individual Re-identification and Postures) dataset is curated from a long-term population of wild Siberian jays studied in Swedish Lapland, supporting re-identification (re-id), action recognition, 2D keypoint estimation, object detection, and instance segm...