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

Sealed in a lake : Biology and conservation of the endangered Saimaa ringed seal

Wildlife species living in proximity with humans often suffer from various anthropogenic factors. Here, we focus on the endangered Saimaa ringed seal (Pusa hispida saimensis), which lives in close connection with humans in Lake Saimaa, Finland. This unique endemic population has remained landlocked since the last glacial period, and it currently consists of only similar to 400 individuals. In this review, we summarize the current knowledge on the Saimaa ringed seal, identify the main risk factors and discuss the efficacy of conservation actions put in place to ensure its long-term survival. The main threats for this rare subspecies are bycatch mortality, habitat destruction and increasingly mild winters. Climate change, together with small population size and an extremely impoverished gene pool, forms a new severe threat. The main conservation actions and priorities for the Saimaa ringed seal are implementation of fishing closures, land-use planning, protected areas, and reduction of pup mortality. Novel innovations, such as provisioning of artificial nest structures, may become increasingly important in the future. Although the Saimaa ringed seal still faces the risk of extinction, the current positive trend in the number of seals shows that endangered wildlife populations can recover even in regions with considerable human inhabitation, when legislative protection is combined with intensive research, engagement of local inhabitants, and innovative conservation actions. Such multifaceted conservation approaches are needed in a world with a growing human population and a rapidly changing climate.Peer reviewe

Identification and recognition of animals from biometric markers using computer vision approaches: a review

Although classic methods (such as ear tagging, marking, etc.) are generally used for animal identification and recognition, biometric methods have gained popularity in recent years due to the advantages they offer. Systems utilizing biometric markers have been developed for various purposes in animal management, including more effective and accurate tracking of animals, vaccination, disease management, and prevention of theft and fraud. Animals" irises, retinas, faces, muzzle, and body patterns contain unique biometric markers. The use of these markers in computer vision approaches for animal identification and tracking systems has become a highly effective and promising research area in recent years. This review aims to provide a general overview of the latest developments in image processing approaches for animal identification and recognition applications. In this review, we examined in detail all relevant studies we could access from different electronic databases for each biometric method. Afterward, the opportunities and challenges of classical and biometric methods were compared. We anticipate that this study, which conducts a literature review on animal identification and recognition based on computer vision approaches, will shed light on future research towards developing automated systems with biometric methods

Pre-trained models are not enough: active and lifelong learning is important for long-term visual monitoring of mammals in biodiversity research—Individual identification and attribute prediction with image features from deep neural networks and decoupled decision models applied to elephants and great apes

Animal re-identification based on image data, either recorded manually by photographers or automatically with camera traps, is an important task for ecological studies about biodiversity and conservation that can be highly automatized with algorithms from computer vision and machine learning. However, fixed identification models only trained with standard datasets before their application will quickly reach their limits, especially for long-term monitoring with changing environmental conditions, varying visual appearances of individuals over time that differ a lot from those in the training data, and new occurring individuals that have not been observed before. Hence, we believe that active learning with human-in-the-loop and continuous lifelong learning is important to tackle these challenges and to obtain high-performance recognition systems when dealing with huge amounts of additional data that become available during the application. Our general approach with image features from deep neural networks and decoupled decision models can be applied to many different mammalian species and is perfectly suited for continuous improvements of the recognition systems via lifelong learning. In our identification experiments, we consider four different taxa, namely two elephant species: African forest elephants and Asian elephants, as well as two species of great apes: gorillas and chimpanzees. Going beyond classical re-identification, our decoupled approach can also be used for predicting attributes of individuals such as gender or age using classification or regression methods. Although applicable for small datasets of individuals as well, we argue that even better recognition performance will be achieved by improving decision models gradually via lifelong learning to exploit huge datasets and continuous recordings from long-term applications. We highlight that algorithms for deploying lifelong learning in real observational studies exist and are ready for use. Hence, lifelong learning might become a valuable concept that supports practitioners when analyzing large-scale image data during long-term monitoring of mammals

Genetic admixture, inbreeding and heritability estimates in captive African cheetahs (Acinonyx jubatus) including linkage analysis for the King cheetah phenotype

This multifaceted study primarily aimed at understanding the genetic make-up of captive versus wild cheetah (Acinonyx jubatus) populations in South Africa, with a specific emphasis on a valuable gene pool of a recessive phenotype that is increasingly being maintained in captive population country-wide. The current literature on cheetah genetics has very little information on diversity levels of wild South African cheetahs, and no information on founder dynamics and genetic make-up of South African captive populations. Decisions on cheetah relocations are being made, implementing current conservation policy, from assumptions on origin and relatedness. This research compared population genetic parameters within the largest South African captive cheetah population to free-ranging Namibian and South African conspecifics. The study addressed concerns regarding excessive Namibian genetic introgression into the native captive population and established the extent of genetic variability and Namibian ancestry within the captive population. The study has attempted to address the rising concern among conservation officials with respect to illegal trade of wild-captured cheetahs, wild caught cheetahs that are sold as captive-bred after implanting a microchip. In addition to establishing routine parentage verification using genetic markers that are polymorphic in this species, this study established a technique powerful enough to estimate ancestry in cheetahs of unknown antecedents. The potential of spatial Bayesian clustering to differentiate the point of origin of unknown cheetahs was exploited and in addition, a database for future forensic efforts to address the problem of illegal trade was established. The captive population that was part of this dataset proved to be quite admixed, excepting for the King lineage which was distinct. The second aspect of this study investigated complex conditions such as development of gastritis, renal conditions and/or susceptibility to infections and its relation to pedigree and marker based inbreeding levels. Heritability values for important breeding traits were estimated from pedigree records of 532 cheetahs and are reported for the first time. Gastritis was weakly correlated to the expression of the King trait. Finally, a smaller cohort of the captive pedigree that segregates for a recessive colour variant called the King phenotype was tested for the assumption that the variation is a mutation of the tabby locus described in domestic cats. Genetic linkage analysis was done by testing microsatellite markers detected linked to Tabby for linkage to a conserved region in the cheetah that potentially codes for the King coat colour. Genetic linkage analysis was not detected between the King locus and the domestic cat microsatellite markers used for this study, with LOD scores remaining non-significant for all the markers.Thesis (PhD)--University of Pretoria, 2011.Production Animal Studiesunrestricte