![]() ![]() ![]() Citizen science platforms have allowed large-scale processing of databases such as camera trap images ( 4) however, ad hoc volunteer coders working independently typically only tag at the species level and cannot solve tasks such as recognizing individual identities. However, exploiting video data is currently severely limited by the amount of human effort required to manually process it, as well as the training and expertise necessary to accurately code such information. The accumulation of decades’ worth of large video databases and archives has immense potential for answering biological questions that require longitudinal data ( 3). Video data have become indispensable in the retrospective analysis and monitoring of wild animal species’ presence, abundance, distribution, and behavior ( 1, 2). Video demo of automated identity and sex recognition of wild chimpanzees at Bossou, achieved through our deep learning pipeline. Metrics of Bossou social networks derived from co-occurrences of detected individuals in video frames. Confusion matrix for the 13 individuals in the test set. Identity and sex recognition results for accuracy on all faces and frontal faces only in the test set. Summary statistics of training and testing datasets for recognition model. Name, ID code, sex, age, and years present for every chimpanzee at Bossou within the dataset analyzed. Frame-level accuracy of model with variation in chimpanzee face resolution. Screenshots from the web-based experiment testing human annotator performance at identifying individual chimpanzees in cropped images.įig. Screenshots of the web-based annotation interfaces.įig. Supplementary material for this article is available at įig. ![]()
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