Automated Object Tracking for Animal Behaviour Studies

Manning, Timmy; Somarriba, Miguel; Roehe, R.; Turner, Simon P.; Wang, Haiying; Zheng, Huiru; Kelly, Brian; Lynch, Jennifer M.; Walsh, Paul

doi:10.1109/bibm47256.2019.8983195

Cited by 11 publications

(3 citation statements)

References 16 publications

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“…The use of object detection and marker recognition algorithms to study invertebrate behaviour is still in its infancy, though recent papers have used such algorithms to behaviour in lab and occasionally wild settings (Hatton-Jones et al, 2021; Petso et al, 2021; Amino and Matsuo, 2022). However, such algorithms have received wider use in studies of vertebrate behaviour and in species identification for camera trapping (Manning et al, 2019; Schindler & Steinhage 2021; Petso et al, 2021).…”

Section: Introductionmentioning

confidence: 99%

Measuring the effect of RFID and Marker Recognition tags on cockroach behaviour using AI aided tracking

McLean,

Fisher

2024

Preprint

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RFID technology and marker recognition algorithms can offer an efficient and non-intrusive means of tracking animal positions. As such, they have become important tools for invertebrate behavioural research. Both approaches require fixing a tag or marker to the study organism, and so it is useful to quantify the effects such procedures have on behaviour before proceeding with further research. However, frequently studies do not report doing such tests. Here, we demonstrate a time-efficient and accessible method for quantifying the impact of tagging on individual movement using open-source automated video tracking software. We tested the effect of RFID tags and tags suitable for marker recognition algorithms on the movement of Argentinian wood roaches (Blapicta dubia) by filming tagged and untagged roaches in laboratory conditions. We employed DeepLabCut on the resultant videos to track cockroach movement and extract measures of behavioural traits. We found no statistically significant differences between RFID tagged and untagged groups in average speed over the trial period, the number of unique zones explored, and the number of discrete walks. However, groups that were tagged with labels for marker recognition had significantly higher values for all three metrics. We therefore support the use of RFID tags to monitor the behaviour ofB. dubiabut note that the effect of using labels suitable for label recognition to identify individuals should be taken into consideration when measuringB.dubiabehaviour. We hope that this study can provide an accessible and viable roadmap for further work investigating the effects of tagging on insect behaviour.

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Section: Introductionmentioning

confidence: 99%

Measuring the effect of RFID and Marker Recognition tags on cockroach behaviour using AI aided tracking

McLean,

Fisher

2024

Preprint

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“…It used to detect the abnormalities in mouse behavior. Machine learning algorithm based study of animal behavior from video is discussed and evaluated in [11].…”

Section: Introductionmentioning

confidence: 99%

Animal Health Monitoring With Missing and Theft Prevention Device Using Wireless Sensor Network and Internet of Things

Sundaramoorthi¹,

P.T²,

M.V³

2020

International Journal of Advances in Signal and Image Sciences

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A device to know the health status of all the animals in a farm along with its missing and theft prevention is proposed in this article. The necessary sensors to sense the health information of animals and the short range wireless communication technology and internet to communicate with the owner of the farm, are planned to use in the devices. This system will be very helpful to diagnose the health condition of all the animals simultaneously and to improve it. The system can prevent the animals from missing or theft. A mobile application which acts as a user interface provides the owner with useful information such as animal's body temperature, heart rate, rumination and their presence inside the farm. The prototype of this idea is developed and is included for testing. Since the animal health status can be monitored from anywhere in world using a mobile application, the device will ease the work of farm owner and the laborers.

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“…To this end, a state-of-the-art neural network object detection technique, Faster R-CNN [18], was chosen as the backbone of the animal detection method for its prioritization of accuracy and precision regardless of object size or density in the image, as opposed to a faster single-shot detector [19]. The Faster R-CNN detector structure has demonstrated its capabilities in both land [20] and marine [21] applications, and is considered a reliable option for challenging tracking tasks.…”

Section: Introductionmentioning

confidence: 99%

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Automated Object Tracking for Animal Behaviour Studies

Cited by 11 publications

References 16 publications

Measuring the effect of RFID and Marker Recognition tags on cockroach behaviour using AI aided tracking

Measuring the effect of RFID and Marker Recognition tags on cockroach behaviour using AI aided tracking

Animal Health Monitoring With Missing and Theft Prevention Device Using Wireless Sensor Network and Internet of Things

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