Action Recognition Using a Spatial-Temporal Network for Wild Felines

Feng, Liqi; Zhao, Yaqin; Sun, Yang; Zhao, Wenxuan; Tang, Jiaxi

doi:10.3390/ani11020485

Cited by 15 publications

(4 citation statements)

References 59 publications

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“…After that, the two characteristic graphs were connected and reduced through a c/r convolution layer. After the convoluted characteristic map passed through the BN (batch normalization) layer, the sigmoid activation function was used to obtain the characteristic map F. The sigmoid activation function [ 29 ] and its formula are as follows:

…”

Section: Methodsmentioning

confidence: 99%

Apple-Net: A Model Based on Improved YOLOv5 to Detect the Apple Leaf Diseases

Zhu

et al. 2022

Plants

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Effective identification of apple leaf diseases can reduce pesticide spraying and improve apple fruit yield, which is significant to agriculture. However, the existing apple leaf disease detection models lack consideration of disease diversity and accuracy, which hinders the application of intelligent agriculture in the apple industry. In this paper, we explore an accurate and robust detection model for apple leaf disease called Apple-Net, improving the conventional YOLOv5 network by adding the Feature Enhancement Module (FEM) and Coordinate Attention (CA) methods. The combination of the feature pyramid and pan in YOLOv5 can obtain richer semantic information and enhance the semantic information of low-level feature maps but lacks the output of multi-scale information. Thus, the FEM was adopted to improve the output of multi-scale information, and the CA was used to improve the detection efficiency. The experimental results show that Apple-Net achieves a higher mAP@0.5 (95.9%) and precision (93.1%) than four classic target detection models, thus proving that Apple-Net achieves more competitive results on apple leaf disease identification.

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…”

Section: Methodsmentioning

confidence: 99%

Apple-Net: A Model Based on Improved YOLOv5 to Detect the Apple Leaf Diseases

Zhu

et al. 2022

Plants

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“…To the best of our knowledge, automated vision-based behavioural analysis has not yet been conducted in free-ranging elephants, and only minimally in other wild free-ranging mammals [113] (e.g. in wild chimpanzees, detecting nut cracking and passing food to mouth [114], or in wild felines, standing, ambling and galloping gait detections). A handful of papers have applied automated pose estimation to ground- and aerial-based videos ([115]: wild apes, [80]: ungulates), but have not explicitly conducted behavioural analyses.…”

Section: Image and Video Monitoringmentioning

confidence: 99%

Elephants and algorithms: a review of the current and future role of AI in elephant monitoring

Brickson,

Zhang,

Vollrath

et al. 2023

J. R. Soc. Interface.

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Artificial intelligence (AI) and machine learning (ML) present revolutionary opportunities to enhance our understanding of animal behaviour and conservation strategies. Using elephants, a crucial species in Africa and Asia’s protected areas, as our focal point, we delve into the role of AI and ML in their conservation. Given the increasing amounts of data gathered from a variety of sensors like cameras, microphones, geophones, drones and satellites, the challenge lies in managing and interpreting this vast data. New AI and ML techniques offer solutions to streamline this process, helping us extract vital information that might otherwise be overlooked. This paper focuses on the different AI-driven monitoring methods and their potential for improving elephant conservation. Collaborative efforts between AI experts and ecological researchers are essential in leveraging these innovative technologies for enhanced wildlife conservation, setting a precedent for numerous other species.

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“…In recent years, with the continuous development of deep learning, animal pose estimation has been widely used in many fields. Feng et al [9] proposed the use of Spatiotemporal networks to combine skeleton features with contour features to automatically identify the actions of cats, thereby assisting in the protection of wild cats. Li et al [10] designed a multi-scale domain adaptation module that proposes a way to learn from synthetic animal data.…”

Section: Introductionmentioning

confidence: 99%

Animal Pose Estimation Algorithm Based on the Lightweight Stacked Hourglass Network

Zhang

Bai

et al. 2023

IEEE Access

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Pose estimation has been a hot topic in the field of machine vision in recent years. Animals exist widely in nature, and the analysis of their shape and movement is important in many fields and industries. In the pose estimation task, to improve the detection accuracy, the existing models often need to consume a lot of computing and memory resources. Therefore, it is a key problem for the pose estimation methods to carry out a lightweight model and reduce the computational overhead on the premise of ensuring model accuracy. In this paper, we focus on the structure of the convolutional neural network in animal pose estimation, construct a lightweight and efficient stacked hourglass network model oriented to optimize the balance of model computation and accuracy, and implement the application algorithm design based on it. Aiming at the problem of large parameters in depthwise convolutional neural networks, a lightweight residual module is proposed, that is, based on the lightweight efficient channel attention improved conditional channel-weighted method (ICCW-Bottle), thereby reducing the weight of the network and obtaining the feature information of different scales. Given the problem that a large amount of feature information is easily lost after the network pooling operation, a lightweight dual-branch fusion module is proposed that fully integrates high-level semantic information and low-level detailed features under the condition of a small number of parameters. Finally, the same as the CC-SSL method: the model is trained jointly using synthetic and real animal datasets, but the CC-SSL method does not take into account the computational power of the model, which consumes a lot of time and memory to run. Through experiments, it is known that compared with the CC-SSL method, the PCK @ 0.05 of this method is increased by 5.5 % on the TigDog dataset. The model in this paper reduces the number of parameters and calculations of the network while ensuring less information loss and model accuracy. The ablation experiment verifies the advancement and effectiveness of the overall network.INDEX TERMS Animal pose estimation, stacked Hourglass Networks, lightweight, residual module, feature fusion.

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Action Recognition Using a Spatial-Temporal Network for Wild Felines

Cited by 15 publications

References 59 publications

Apple-Net: A Model Based on Improved YOLOv5 to Detect the Apple Leaf Diseases

Apple-Net: A Model Based on Improved YOLOv5 to Detect the Apple Leaf Diseases

Elephants and algorithms: a review of the current and future role of AI in elephant monitoring

Animal Pose Estimation Algorithm Based on the Lightweight Stacked Hourglass Network

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