Combining feature aggregation and geometric similarity for re-identification of patterned animals

Image-based re-identification of animal individuals allows gathering of information such as migration patterns of the animals over time. This, together with large image volumes collected using camera traps and crowdsourcing, opens novel possibilities to study animal populations. For many species, the re-identification can be done by analyzing the permanent fur, feather, or skin patterns that are unique to each individual. In this paper, we address the re-identification by combining two types of pattern similarity metrics: 1) pattern appearance similarity obtained by pattern feature aggregation and 2) geometric pattern similarity obtained by analyzing the geometric consistency of pattern similarities. The proposed combination allows to efficiently utilize both the local and global pattern features, providing a general re-identification approach that can be applied to a wide variety of different pattern types. In the experimental part of the work, we demonstrate that the method achieves promising re-identification accuracies for Saimaa ringed seals and whale sharks.Comment: Camera traps, AI, and Ecology, 3rd International Worksho

Distant learning as an innovative approach to the implementation of the practice-oriented discipline “Physical Culture and Sport”

Physical culture lessons during difficult epidemiological situation in the country and in the world are very important, however, distant lessons organization can be a problem. In this connection it is urgent to search for the methods and techniques of physical culture lessons organization. The article presents the variants of distant education (in terms of isolation) in “Physical culture”discipline. These variants description has practical importance. The authors make an attempt to reveal students’ attitude to distant learning in discipline “Physical culture and sport” from different positions: general attitude of students to this kind of education; the most acceptable for students variants of distant learning; difficulties, which appear in terms of this kind of lessons organization; convenience of using different platforms

SealID: Saimaa Ringed Seal Re-Identification Dataset

Wildlife camera traps and crowd-sourced image material provide novel possibilities to monitor endangered animal species. The massive data volumes call for automatic methods to solve various tasks related to population monitoring, such as the re-identification of individual animals. The Saimaa ringed seal (Pusa hispida saimensis) is an endangered subspecies only found in Lake Saimaa, Finland, and is one of the few existing freshwater seal species. Ringed seals have permanent pelage patterns that are unique to each individual and that can be used for the identification of individuals. A large variation in poses, further exacerbated by the deformable nature of seals, together with varying appearance and low contrast between the ring pattern and the rest of the pelage makes the Saimaa ringed seal re-identification task very challenging, providing a good benchmark by which to evaluate state-of-the-art re-identification methods. Therefore, we make our Saimaa ringed seal image (SealID) dataset (N = 57) publicly available for research purposes. In this paper, the dataset is described, the evaluation protocol for re-identification methods is proposed, and the results for two baseline methods—HotSpotter and NORPPA—are provided. The SealID dataset has been made publicly available