50 research outputs found
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
Automatic Individual Identification of Patterned Solitary Species Based on Unlabeled Video Data
The manual processing and analysis of videos from camera traps is
time-consuming and includes several steps, ranging from the filtering of
falsely triggered footage to identifying and re-identifying individuals. In
this study, we developed a pipeline to automatically analyze videos from camera
traps to identify individuals without requiring manual interaction. This
pipeline applies to animal species with uniquely identifiable fur patterns and
solitary behavior, such as leopards (Panthera pardus). We assumed that the same
individual was seen throughout one triggered video sequence. With this
assumption, multiple images could be assigned to an individual for the initial
database filling without pre-labeling. The pipeline was based on
well-established components from computer vision and deep learning,
particularly convolutional neural networks (CNNs) and scale-invariant feature
transform (SIFT) features. We augmented this basis by implementing additional
components to substitute otherwise required human interactions. Based on the
similarity between frames from the video material, clusters were formed that
represented individuals bypassing the open set problem of the unknown total
population. The pipeline was tested on a dataset of leopard videos collected by
the Pan African Programme: The Cultured Chimpanzee (PanAf) and achieved a
success rate of over 83% for correct matches between previously unknown
individuals. The proposed pipeline can become a valuable tool for future
conservation projects based on camera trap data, reducing the work of manual
analysis for individual identification, when labeled data is unavailable
Advanced concepts for intelligent vision systems, 19th international conference, ACIVS 2018, proceedings
International audienceThis book constitutes the refereed proceedings of the 19th International Conference on Advanced Concepts for Intelligent Vision Systems, ACIVS 2018, held in Poitiers, France, in September 2018. The 52 full papers presented in this volume were carefully reviewed and selected from 91 submissions. They were organized in topical sections named: video analysis; segmentation and classification; remote sending; biometrics; deep learning; coding and compression; and image restauration and reconstruction
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