Giant panda behaviour recognition using images

Swarup, Pranjal; Peng, Chen; Hou, Rong; Que, Pinjia; Liu, Peng; Kong, Adams Wai-Kin

doi:10.1016/j.gecco.2021.e01510

Cited by 7 publications

(5 citation statements)

References 14 publications

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“…Our work aims to close this gap in research. [20] pose estimation Swarup et al [21] activity recognition DeepLabCut [22] pose estimation Nilsson et al [23] count Kashiha et al [24] locomotion Blyzer [16] trajectory idTracker [17] trajectory GroupTracker [18] trajectory Our Framework trajectory…”

Section: Introductionmentioning

confidence: 99%

Automated Video-Based Analysis Framework for Behavior Monitoring of Individual Animals in Zoos Using Deep Learning—A Study on Polar Bears

Zuerl

Stoll

Brehm

et al. 2022

Animals

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The monitoring of animals under human care is a crucial tool for biologists and zookeepers to keep track of the animals’ physical and psychological health. Additionally, it enables the analysis of observed behavioral changes and helps to unravel underlying reasons. Enhancing our understanding of animals ensures and improves ex situ animal welfare as well as in situ conservation. However, traditional observation methods are time- and labor-intensive, as they require experts to observe the animals on-site during long and repeated sessions and manually score their behavior. Therefore, the development of automated observation systems would greatly benefit researchers and practitioners in this domain. We propose an automated framework for basic behavior monitoring of individual animals under human care. Raw video data are processed to continuously determine the position of the individuals within the enclosure. The trajectories describing their travel patterns are presented, along with fundamental analysis, through a graphical user interface (GUI). We evaluate the performance of the framework on captive polar bears (Ursus maritimus). We show that the framework can localize and identify individual polar bears with an F1 score of 86.4%. The localization accuracy of the framework is 19.9±7.6 cm, outperforming current manual observation methods. Furthermore, we provide a bounding-box-labeled dataset of the two polar bears housed in Nuremberg Zoo.

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Section: Introductionmentioning

confidence: 99%

Automated Video-Based Analysis Framework for Behavior Monitoring of Individual Animals in Zoos Using Deep Learning—A Study on Polar Bears

Zuerl

Stoll

Brehm

et al. 2022

Animals

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“…When fresh feces were found in the wild, researchers preserved the fecal samples in 100% ethanol. The panda’s intestinal cells in the outer mucosa of the feces were then isolated to extract DNA [ 10 ]. The DNA-based approach has very high requirements for panda feces, and only fresh feces that retain a mucous membrane can be used.…”

Section: Introductionmentioning

confidence: 99%

Automatic Recognition of Giant Panda Attributes from Their Vocalizations Based on Squeeze-and-Excitation Network

Zhao

Zhang

Hou

et al. 2022

Sensors

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The giant panda (Ailuropoda melanoleuca) has long attracted the attention of conservationists as a flagship and umbrella species. Collecting attribute information on the age structure and sex ratio of the wild giant panda populations can support our understanding of their status and the design of more effective conservation schemes. In view of the shortcomings of traditional methods, which cannot automatically recognize the age and sex of giant pandas, we designed a SENet (Squeeze-and-Excitation Network)-based model to automatically recognize the attributes of giant pandas from their vocalizations. We focused on the recognition of age groups (juvenile and adult) and sex of giant pandas. The reason for using vocalizations is that among the modes of animal communication, sound has the advantages of long transmission distances, strong penetrating power, and rich information. We collected a dataset of calls from 28 captive giant panda individuals, with a total duration of 1298.02 s of recordings. We used MFCC (Mel-frequency Cepstral Coefficients), which is an acoustic feature, as inputs for the SENet. Considering that small datasets are not conducive to convergence in the training process, we increased the size of the training data via SpecAugment. In addition, we used focal loss to reduce the impact of data imbalance. Our results showed that the F1 scores of our method for recognizing age group and sex reached 96.46% ± 5.71% and 85.85% ± 7.99%, respectively, demonstrating that the automatic recognition of giant panda attributes based on their vocalizations is feasible and effective. This more convenient, quick, timesaving, and laborsaving attribute recognition method can be used in the investigation of wild giant pandas in the future.

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“…The aim of this paper is to automate animal behavior recognition in wild footage. Deep learning–based behavior recognition has thus far been shown in constrained laboratory settings ( 4 , 5 ) or using still images ( 6 ) and has yet to be effectively demonstrated on unconstrained video footage recorded in the wild. Measuring animal behavior from wild footage presents substantial challenges—often, behaviors are hard to detect, obscured by motion blur, occlusion, vegetation, poor resolution, or lighting.…”

Section: Introductionmentioning

confidence: 99%