4,583 research outputs found
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
Automated Visual Fin Identification of Individual Great White Sharks
This paper discusses the automated visual identification of individual great
white sharks from dorsal fin imagery. We propose a computer vision photo ID
system and report recognition results over a database of thousands of
unconstrained fin images. To the best of our knowledge this line of work
establishes the first fully automated contour-based visual ID system in the
field of animal biometrics. The approach put forward appreciates shark fins as
textureless, flexible and partially occluded objects with an individually
characteristic shape. In order to recover animal identities from an image we
first introduce an open contour stroke model, which extends multi-scale region
segmentation to achieve robust fin detection. Secondly, we show that
combinatorial, scale-space selective fingerprinting can successfully encode fin
individuality. We then measure the species-specific distribution of visual
individuality along the fin contour via an embedding into a global `fin space'.
Exploiting this domain, we finally propose a non-linear model for individual
animal recognition and combine all approaches into a fine-grained
multi-instance framework. We provide a system evaluation, compare results to
prior work, and report performance and properties in detail.Comment: 17 pages, 16 figures. To be published in IJCV. Article replaced to
update first author contact details and to correct a Figure reference on page
ORCA-SPOT: An Automatic Killer Whale Sound Detection Toolkit Using Deep Learning
Large bioacoustic archives of wild animals are an important source to identify reappearing communication patterns, which can then be related to recurring behavioral patterns to advance the current understanding of intra-specific communication of non-human animals. A main challenge remains that most large-scale bioacoustic archives contain only a small percentage of animal vocalizations and a large amount of environmental noise, which makes it extremely difficult to manually retrieve sufficient vocalizations for further analysis – particularly important for species with advanced social systems and complex vocalizations. In this study deep neural networks were trained on 11,509 killer whale (Orcinus orca) signals and 34,848 noise segments. The resulting toolkit ORCA-SPOT was tested on a large-scale bioacoustic repository – the Orchive – comprising roughly 19,000 hours of killer whale underwater recordings. An automated segmentation of the entire Orchive recordings (about 2.2 years) took approximately 8 days. It achieved a time-based precision or positive-predictive-value (PPV) of 93.2% and an area-under-the-curve (AUC) of 0.9523. This approach enables an automated annotation procedure of large bioacoustics databases to extract killer whale sounds, which are essential for subsequent identification of significant communication patterns. The code will be publicly available in October 2019 to support the application of deep learning to bioaoucstic research. ORCA-SPOT can be adapted to other animal species
Towards Automated Biometric Identification of Sea Turtles (Chelonia mydas)
Passive biometric identification enables wildlife monitoring with minimal disturbance. Using a motion-activated camera placed at an elevated position and facing downwards, images of sea turtle carapaces were collected, each belonging to one of sixteen Chelonia mydas juveniles. Then, co-variant and robust image descriptors from these images were learned, enabling indexing and retrieval. In this paper, several classification results of sea turtle carapaces using the learned image descriptors are presented. It was found that a template-based descriptor, i.e. Histogram of Oriented Gradients (HOG) performed much better during classification than keypoint-based descriptors. For our dataset, a high-dimensional descriptor is a must because of the minimal gradient and color information in the carapace images. Using HOG, we obtained an average classification accuracy of 65%.
A DNA biochip for on-the-spot multiplexed pathogen identification
Miniaturized integrated DNA analysis systems have largely been based on a multi-chamber design with microfluidic control to process the sample sequentially from one module to another. This microchip design in connection with optics involved hinders the deployment of this technology for point-of-care applications. In this work, we demonstrate the implementation of sample preparation, DNA amplification, and electrochemical detection in a single silicon and glass-based microchamber and its application for the multiplexed detection of Escherichia coli and Bacillus subtilis cells. The microdevice has a thin-film heater and temperature sensor patterned on the silicon substrate. An array of indium tin oxide (ITO) electrodes was constructed within the microchamber as the transduction element. Oligonucleotide probes specific to the target amplicons are individually positioned at each ITO surface by electrochemical copolymerization of pyrrole and pyrrole−probe conjugate. These immobilized probes were stable to the thermal cycling process and were highly selective. The DNA-based identification of the two model pathogens involved a number of steps including a thermal lysis step, magnetic particle-based isolation of the target genomes, asymmetric PCR, and electrochemical sequence-specific detection using silver-enhanced gold nanoparticles. The microchamber platform described here offers a cost-effective and sample-to-answer technology for on-site monitoring of multiple pathogens
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
A DNA biochip for on-the-spot multiplexed pathogen identification
Miniaturized integrated DNA analysis systems have largely been based on a multi-chamber design with microfluidic control to process the sample sequentially from one module to another. This microchip design in connection with optics involved hinders the deployment of this technology for point-of-care applications. In this work, we demonstrate the implementation of sample preparation, DNA amplification, and electrochemical detection in a single silicon and glass-based microchamber and its application for the multiplexed detection of Escherichia coli and Bacillus subtilis cells. The microdevice has a thin-film heater and temperature sensor patterned on the silicon substrate. An array of indium tin oxide (ITO) electrodes was constructed within the microchamber as the transduction element. Oligonucleotide probes specific to the target amplicons are individually positioned at each ITO surface by electrochemical copolymerization of pyrrole and pyrrole−probe conjugate. These immobilized probes were stable to the thermal cycling process and were highly selective. The DNA-based identification of the two model pathogens involved a number of steps including a thermal lysis step, magnetic particle-based isolation of the target genomes, asymmetric PCR, and electrochemical sequence-specific detection using silver-enhanced gold nanoparticles. The microchamber platform described here offers a cost-effective and sample-to-answer technology for on-site monitoring of multiple pathogens
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