Automated Visual Fin Identification of Individual Great White Sharks

Hughes, Benjamin; Burghardt, Tilo

doi:10.1007/s11263-016-0961-y

Cited by 37 publications

(35 citation statements)

References 48 publications

(73 reference statements)

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“…AMWR's test precision (74%) and recall (80%) results (last column of Table 1) were better than the corresponding state-of-the-art gorilla identification results [11] of approximately 60%. The AMWR's test accuracy (93%) and precision (74%) were comparable to the 81% average precision achieved in the state-of-the-art great white shark identification results [9]. The validation and test prediction metrics were comparable (third and fourth columns in Table 1) supporting the achieved testvalues to be the expected benchmark/baseline values of the AMWR model in future similar circumstances/studies.…”

Section: Resultssupporting

confidence: 64%

“…Similarly, all available 36 MW1020 validation images were used with 36 randomly selected negative validation images, where a new random selection of 36 negative images was done before each training cycle. Also due to the highly unbalanced number of positive and negative examples, AMWR classifier was assessed via precision, recall, fprate (false-positive), in addition to the standard accuracy [9] [11] [27],…”

Section: Resultsmentioning

confidence: 99%

“…The automation of the photo-ID process is often highly specific to the required species, e.g. fin contour of great white sharks [9]. Due to its fundamental research role, photo-ID is an active research area for many species, e.g.…”

Section: Introductionmentioning

confidence: 99%

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Individual Minke Whale Recognition Using Deep Learning Convolutional Neural Networks

Konovalov¹,

Hillcoat²,

Williams³

et al. 2018

GEP

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The only known predictable aggregation of dwarf minke whales (Balaenoptera acutorostrata subsp.) occurs in the Australian offshore waters of the northern Great Barrier Reef in May-August each year. The identification of individual whales is required for research on the whales' population characteristics and for monitoring the potential impacts of tourism activities, including commercial swims with the whales. At present, it is not cost-effective for researchers to manually process and analyze the tens of thousands of underwater images collated after each observation/tourist season, and a large data base of historical non-identified imagery exists. This study reports the first proof of concept for recognizing individual dwarf minke whales using the Deep Learning Convolutional Neural Networks (CNN).The "off-the-shelf" Image net-trained VGG16 CNN was used as the feature-encoder of the per-pixel sematic segmentation Automatic Minke Whale Recognizer (AMWR). The most frequently photographed whale in a sample of 76 individual whales (MW1020) was identified in 179 images out of the total 1320 images provided. Training and image augmentation procedures were developed to compensate for the small number of available images. The trained AMWR achieved 93% prediction accuracy on the testing subset of 36 positive/MW1020 and 228 negative/not-MW1020 images, where each negative image contained at least one of the other 75 whales. Furthermore on the test subset, AMWR achieved 74% precision, 80% recall, and 4% false-positive rate, making the presented approach comparable or better to other state-of-the-art individual animal recognition results.

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Section: Resultssupporting

confidence: 64%

Section: Resultsmentioning

confidence: 99%

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Individual Minke Whale Recognition Using Deep Learning Convolutional Neural Networks

Konovalov¹,

Hillcoat²,

Williams³

et al. 2018

GEP

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“…In contrast to purely fine-grained category recognition, we are interested in the identification of individual animals [16,26] (or instances) of a single species (or category) rather than highly similar categories. Whilst animal biometrics [36] may operate on a wide variety of entities to achieve identification, our technical focus of the review will be solely on techniques applicable to facial identification.…”

Section: Automated Detection and Identificationmentioning

confidence: 99%

Towards Automated Visual Monitoring of Individual Gorillas in the Wild

Brust

Burghardt

Groenenberg

et al. 2017

2017 IEEE International Conference on Computer Vision Workshops (ICCVW)

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In this paper we report on the context and evaluation of a system for an automatic interpretation of sightings of individual western lowland gorillas (Gorilla gorilla gorilla) as captured in facial fieldVisual data acquisition in the field often captures sufficient information to establish encounters with individual gorillas. However, relevant information is locked within the pixel patterns measured, usually requiring expert knowledge and time-consuming efforts for identification. Computer vision can help to extract gorilla identities by performing automated species detection, followed by individual facial identification. We show that standard deep learning models combined with a traditional SVM classifier can be used for this task. To assist encounter processing, predictions can be presented graphically with known population information as shown.

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“…In [11], the authors propose a trailing edge indexing algorithm and apply it to great white sharks. After defining keypoints for feature extraction by convolving the contour with a Difference-of-Gaussian kernel, they explore the use of both the Difference-of-Gaussian norm and the descriptor from [2].…”

Section: Related Workmentioning

confidence: 99%

Integral Curvature Representation and Matching Algorithms for Identification of Dolphins and Whales

Weideman

Jablons

Holmberg³

et al. 2017

2017 IEEE International Conference on Computer Vision Workshops (ICCVW)

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We address the problem of identifying individual cetaceans from images showing the trailing edge of their fins. Given the trailing edge from an unknown individual, we produce a ranking of known individuals from a database. The nicks and notches along the trailing edge define an individual's unique signature. We define a representation based on integral curvature that is robust to changes in viewpoint and pose, and captures the pattern of nicks and notches in a local neighborhood at multiple scales. We explore two ranking methods that use this representation. The first uses a dynamic programming time-warping algorithm to align two representations, and interprets the alignment cost as a measure of similarity. This algorithm also exploits learned spatial weights to downweight matches from regions of unstable curvature. The second interprets the representation as a feature descriptor. Feature keypoints are defined at the local extrema of the representation. Descriptors for the set of known individuals are stored in a tree structure, which allows us to perform queries given the descriptors from an unknown trailing edge. We evaluate the top-k accuracy on two real-world datasets to demonstrate the effectiveness of the curvature representation, achieving top-1 accuracy scores of approximately 95% and 80% for bottlenose dolphins and humpback whales, respectively.

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Automated Visual Fin Identification of Individual Great White Sharks

Cited by 37 publications

References 48 publications

Individual Minke Whale Recognition Using Deep Learning Convolutional Neural Networks

Individual Minke Whale Recognition Using Deep Learning Convolutional Neural Networks

Towards Automated Visual Monitoring of Individual Gorillas in the Wild

Integral Curvature Representation and Matching Algorithms for Identification of Dolphins and Whales

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