Wireless 3D localization of animals for trait and behavior analysis in indoor and outdoor areas

Millner, H.; Ebelt, Randolf; Hoffmann, Gundula; Vossiek, Martin

doi:10.1109/imws2.2009.5307890

Cited by 4 publications

(1 citation statement)

References 7 publications

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“…L. Catarinucci used a novel RFID-based approach that enabled the effective localization and tracking of small-sized laboratory animals, and tests were conducted on rats to prove the validity of the method [5]. H. Millner proposed a system for the 3D indoor and outdoor localization of animals using a sequential Monte Carlo method by incorporating the dynamics of the target object [6]. The core part of outdoor positioning is the use of satellite positioning systems to determine the latitude and longitude information of livestock, which is then combined with other sensors and wireless communication technologies to achieve a wider range of applications [7].…”

Section: Introductionmentioning

confidence: 99%

Detection and Localization of Albas Velvet Goats Based on YOLOv4

Guo,

Wang,

Han

et al. 2023

Animals

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In order to achieve goat localization to help prevent goats from wandering, we proposed an efficient target localization method based on machine vision. Albas velvet goats from a farm in Ertok Banner, Ordos City, Inner Mongolia Autonomous Region, China, were the main objects of study. First, we proposed detecting the goats using a shallow convolutional neural network, ShallowSE, with the channel attention mechanism SENet, the GeLU activation function and layer normalization. Second, we designed three fully connected coordinate regression network models to predict the spatial coordinates of the goats. Finally, the target detection algorithm and the coordinate regression algorithm were combined to localize the flock. We experimentally confirmed the proposed method using our dataset. The proposed algorithm obtained a good detection accuracy and successful localization rate compared to other popular algorithms. The overall number of parameters in the target detection algorithm model was only 4.5 M. The average detection accuracy reached 95.89% and the detection time was only 8.5 ms. The average localization error of the group localization algorithm was only 0.94 m and the localization time was 0.21 s. In conclusion, the method achieved fast and accurate localization, which helped to rationalize the use of grassland resources and to promote the sustainable development of rangelands.

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Section: Introductionmentioning

confidence: 99%

Detection and Localization of Albas Velvet Goats Based on YOLOv4

Guo,

Wang,

Han

et al. 2023

Animals

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Steady Delivery: Wireless Local Positioning Systems for Tracking and Autonomous Navigation of Transport Vehicles and Mobile Robots

Dobrev

Vossiek

Christmann³

et al. 2017

IEEE Microwave

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IoT-Based Information System for Healthcare Application: Design Methodology Approach

Dziak

Jachimczyk²,

Kulesza

2017

Applied Sciences

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Abstract:Over the last few decades, life expectancy has increased significantly. However, elderly people who live on their own often need assistance due to mobility difficulties, symptoms of dementia or other health problems. In such cases, an autonomous supporting system may be helpful. This paper proposes the Internet of Things (IoT)-based information system for indoor and outdoor use. Since the conducted survey of related works indicated a lack of methodological approaches to the design process, therefore a Design Methodology (DM), which approaches the design target from the perspective of the stakeholders, contracting authorities and potential users, is introduced. The implemented solution applies the three-axial accelerometer and magnetometer, Pedestrian Dead Reckoning (PDR), thresholding and the decision trees algorithm. Such an architecture enables the localization of a monitored person within four room-zones with accuracy; furthermore, it identifies falls and the activities of lying, standing, sitting and walking. Based on the identified activities, the system classifies current activities as normal, suspicious or dangerous, which is used to notify the healthcare staff about possible problems. The real-life scenarios validated the high robustness of the proposed solution. Moreover, the test results satisfied both stakeholders and future users and ensured further cooperation with the project.

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Wireless 3D localization of animals for trait and behavior analysis in indoor and outdoor areas

Cited by 4 publications

References 7 publications

Detection and Localization of Albas Velvet Goats Based on YOLOv4

Detection and Localization of Albas Velvet Goats Based on YOLOv4

Steady Delivery: Wireless Local Positioning Systems for Tracking and Autonomous Navigation of Transport Vehicles and Mobile Robots

IoT-Based Information System for Healthcare Application: Design Methodology Approach

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