Automatic Detection and Recognition of Individuals in Patterned Species

Cheema, Gullal S.; Anand, Sandeep

doi:10.1007/978-3-319-71273-4_3

Cited by 34 publications

(18 citation statements)

References 21 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…A recent review of different methods for animal recognition can be found in (Schneider, Taylor, Linquist, & Kremer, 2019). Some animals like zebra, tiger, and giraffe have highly unique patterns composed of stripes, patches, or spots on their coats, which benefit the image‐based animal recognition (Burghardt & Campbell, 2007; Cheema & Anand, 2017; Kumar & Singh, 2016). However, pandas have a very similar appearance in terms of coat patterns, which makes it challenging to distinguish them based on images.…”

Section: Introductionmentioning

confidence: 99%

A study on giant panda recognition based on images of a large proportion of captive pandas

Chen

Swarup

Matkowski

et al. 2020

Ecology and Evolution

View full text Add to dashboard Cite

As a highly endangered species, the giant panda (panda) has attracted significant attention in the past decades. Considerable efforts have been put on panda conservation and reproduction, offering the promising outcome of maintaining the population size of pandas. To evaluate the effectiveness of conservation and management strategies, recognizing individual pandas is critical. However, it remains a challenging task because the existing methods, such as traditional tracking method, discrimination method based on footprint identification, and molecular biology method, are invasive, inaccurate, expensive, or challenging to perform. The advances of imaging technologies have led to the wide applications of digital images and videos in panda conservation and management, which makes it possible for individual panda recognition in a noninvasive manner by using image‐based panda face recognition method. In recent years, deep learning has achieved great success in the field of computer vision and pattern recognition. For panda face recognition, a fully automatic deep learning algorithm which consists of a sequence of deep neural networks (DNNs) used for panda face detection, segmentation, alignment, and identity prediction is developed in this study. To develop and evaluate the algorithm, the largest panda image dataset containing 6,441 images from 218 different pandas, which is 39.78% of captive pandas in the world, is established. The algorithm achieved 96.27% accuracy in panda recognition and 100% accuracy in detection. This study shows that panda faces can be used for panda recognition. It enables the use of the cameras installed in their habitat for monitoring their population and behavior. This noninvasive approach is much more cost‐effective than the approaches used in the previous panda surveys.

show abstract

Section: Introductionmentioning

confidence: 99%

A study on giant panda recognition based on images of a large proportion of captive pandas

Chen

Swarup

Matkowski

et al. 2020

Ecology and Evolution

View full text Add to dashboard Cite

show abstract

“…As previously discussed, various researchers are now applying image analysis, and in particular image recognition techniques, to classify images from camera traps. A typical objective is to see how well various recognition algorithms identify animal species (e.g., Norouzzadeha et al, ; Schneider et al, ; Tabak et al, ; Yousif et al, ), and even in recognizing individuals in particular species (e.g., Cheema & Anand, ; Crouse et al, ). Simpler image analysis methods can also identify other image aspects, for example, differentiate between color versus monochrome images, light versus dark images, and so on.…”

Section: Issue: Entering Datamentioning

confidence: 99%

“…The process can be repeated for other metadata of interest. A typical objective is to see how well various recognition algorithms identify animal species (e.g., Norouzzadeha et al, 2018;Schneider et al, 2018;Tabak et al, 2018;Yousif et al, 2019), and even in recognizing individuals in particular species (e.g., Cheema & Anand, 2017;Crouse et al, 2017 porates an image analyser that automatically classifies images against a user-configurable darkness threshold. Its classification is recorded in the "Image Quality" data field of every image as either "Dark" or "Ok." Timelapse also includes the ability to filter the displayed images by its data, which we will discuss shortly.…”

Section: Standard File Information Timelapse Template Schemas Alwaysmentioning

confidence: 99%

Design patterns for wildlife‐related camera trap image analysis

Greenberg

Godin

Whittington

2019

Ecology and Evolution

View full text Add to dashboard Cite

This paper describes and explains design patterns for software that supports how analysts can efficiently inspect and classify camera trap images for wildlife‐related ecological attributes. Broadly speaking, a design pattern identifies a commonly occurring problem and a general reusable design approach to solve that problem. A developer can then use that design approach to create a specific software solution appropriate to the particular situation under consideration. In particular, design patterns for camera trap image analysis by wildlife biologists address solutions to commonly occurring problems they face while inspecting a large number of images and entering ecological data describing image attributes. We developed design patterns for image classification based on our understanding of biologists' needs that we acquired over 8 years during development and application of the freely available Timelapse image analysis system. For each design pattern presented, we describe the problem, a design approach that solves that problem, and a concrete example of how Timelapse addresses the design pattern. Our design patterns offer both general and specific solutions related to: maintaining data consistency, efficiencies in image inspection, methods for navigating between images, efficiencies in data entry including highly repetitious data entry, and sorting and filtering image into sequences, episodes, and subsets. These design patterns can inform the design of other camera trap systems and can help biologists assess how competing software products address their project‐specific needs along with determining an efficient workflow.

show abstract

“…Besides, manual identification relies on the experience of operators and is highly subjective. If the number of individuals is large, it is less feasible to identify individuals within a species manually (Willi et al 2019;Cheema & Anand 2017). Therefore, semi-automatic or fully-automatic identification methods have been a focus of research in computer vision and machine learning (Yousef et al 2018).…”

Section: Introductionmentioning

confidence: 99%

Amur tiger stripes: individual identification based on deep convolutional neural network

Shi

Liú²,

Cui

et al. 2020

Integr. Zool.

View full text Add to dashboard Cite

The automatic individual identification of Amur tigers (Panthera tigris altaica) is important for population monitoring and making effective conservation strategies. Most existing research primarily relies on manual identification, which does not scale well to large datasets. In this paper, the deep convolution neural networks algorithm is constructed to implement the automatic individual identification for large numbers of Amur tiger images. The experimental data were obtained from 40 Amur tigers in Tieling Guaipo Tiger Park, China. The number of images collected from each tiger was approximately 200, and a total of 8277 images were obtained. The experiments were carried out on both the left and right side of body. Our results suggested that the recognition accuracy rate of left and right sides are 90.48% and 93.5%, respectively. The accuracy of our network has achieved the similar level compared to other state of the art networks like LeNet, ResNet34, and ZF_Net. The running time is much shorter than that of other networks. Consequently, this study can provide a new approach on automatic individual identification technology in the case of the Amur tiger.

show abstract

Automatic Detection and Recognition of Individuals in Patterned Species

Cited by 34 publications

References 21 publications

A study on giant panda recognition based on images of a large proportion of captive pandas

A study on giant panda recognition based on images of a large proportion of captive pandas

Design patterns for wildlife‐related camera trap image analysis

Amur tiger stripes: individual identification based on deep convolutional neural network

Contact Info

Product

Resources

About