A review of 28 free animal-tracking software applications: current features and limitations

Panadeiro, Verónica; Rodríguez, Almudena Suárez; Henry, Jason; Wlodkowic, Donald; Andersson, Magnus

doi:10.1038/s41684-021-00811-1

Cited by 57 publications

(60 citation statements)

References 52 publications

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“…Still, accurate and robust maintenance of individual identities within a group remains a major challenge of automated video tracking systems, especially if inbred mice are used which are (almost) indistinguishable by size, color, fur pattern, and likely body shape/contour. The increasing interest in multiple animal tracking with correct identification preservation associated with group-housed conditions has led to a very large number of different video tracking systems and algorithms being available or under development, for a large variety of species and experimental environments [freely available animal tracking software was recently reviewed by Panadeiro et al (2021) ]. Several video tracking systems have already been successfully applied in socially housed rodents, either in an observation cage or open-field arena, usually lacking the supply of drink, feed, and shelter.…”

Section: Measuring Behavioral Traitsmentioning

confidence: 99%

Measuring Locomotor Activity and Behavioral Aspects of Rodents Living in the Home-Cage

Klein

Budiman²,

Homberg

et al. 2022

Front. Behav. Neurosci.

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Automatization and technological advances have led to a larger number of methods and systems to monitor and measure locomotor activity and more specific behavior of a wide variety of animal species in various environmental conditions in laboratory settings. In rodents, the majority of these systems require the animals to be temporarily taken away from their home-cage into separate observation cage environments which requires manual handling and consequently evokes distress for the animal and may alter behavioral responses. An automated high-throughput approach can overcome this problem. Therefore, this review describes existing automated methods and technologies which enable the measurement of locomotor activity and behavioral aspects of rodents in their most meaningful and stress-free laboratory environment: the home-cage. In line with the Directive 2010/63/EU and the 3R principles (replacement, reduction, refinement), this review furthermore assesses their suitability and potential for group-housed conditions as a refinement strategy, highlighting their current technological and practical limitations. It covers electrical capacitance technology and radio-frequency identification (RFID), which focus mainly on voluntary locomotor activity in both single and multiple rodents, respectively. Infrared beams and force plates expand the detection beyond locomotor activity toward basic behavioral traits but discover their full potential in individually housed rodents only. Despite the great premises of these approaches in terms of behavioral pattern recognition, more sophisticated methods, such as (RFID-assisted) video tracking technology need to be applied to enable the automated analysis of advanced behavioral aspects of individual animals in social housing conditions.

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Section: Measuring Behavioral Traitsmentioning

confidence: 99%

Measuring Locomotor Activity and Behavioral Aspects of Rodents Living in the Home-Cage

Klein

Budiman²,

Homberg

et al. 2022

Front. Behav. Neurosci.

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“…B-SOiD analyzes videos acquired from different perspectives, showing the best results from bottom-up recordings. For further discussion and comparison between these tools refer to Panadeiro et al ( 2021 ); von Ziegler et al ( 2021 ). In contrast with other tools, PyRAT can be used in any operational system, does not need pre-trained classifiers, works without a graphic interface, and provides interactive documentation using Jupyter notebooks.…”

Section: Discussionmentioning

confidence: 99%

PyRAT: An Open-Source Python Library for Animal Behavior Analysis

Almeida¹,

Spinelli²,

Lima³

et al. 2022

Front. Neurosci.

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Here we developed an open-source Python-based library called Python rodent Analysis and Tracking (PyRAT). Our library analyzes tracking data to classify distinct behaviors, estimate traveled distance, speed and area occupancy. To classify and cluster behaviors, we used two unsupervised algorithms: hierarchical agglomerative clustering and t-distributed stochastic neighbor embedding (t-SNE). Finally, we built algorithms that associate the detected behaviors with synchronized neural data and facilitate the visualization of this association in the pixel space. PyRAT is fully available on GitHub: https://github.com/pyratlib/pyrat.

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“…In such a context, the proposed strategy could significantly contribute to the gathering of visual evidence against criminal acts or reinforce the border control mechanisms [ 32 , 33 , 34 , 35 ]. Covert military operations have been already mentioned, but homeland security missions, securing areas, convoy protection [ 36 ], etc., could also benefit from this approach; in sporting events [ 37 , 38 ], where the goal is to simultaneously follow several targets of interest, while minimizing intrusion in the scene; cinematographic and audiovisual works [ 39 , 40 ], with the intent of simultaneously shooting several points of view from the same trajectory in the air; wildlife monitoring, attempting to study animal behavior in relatively open areas [ 41 , 42 ], with minimal intrusion into the environment; and in traffic surveillance applications, enabling simultaneous tracking and close-up filming of the behavior of several vehicles [ 43 , 44 ].…”

Section: State-of-the-art and The Main Contributionsmentioning

confidence: 99%

Flying Chameleons: A New Concept for Minimum-Deployment, Multiple-Target Tracking Drones

Vargas

Vivas

Rubio

et al. 2022

Sensors

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In this paper, we aim to open up new perspectives in the field of autonomous aerial surveillance and target tracking systems, by exploring an alternative that, surprisingly, and to the best of the authors’ knowledge, has not been addressed in that context by the research community thus far. It can be summarized by the following two questions. Under the scope of such applications, what are the implications and possibilities offered by mounting several steerable cameras onboard of each aerial agent? Second, how can optimization algorithms benefit from this new framework, in their attempt to provide more efficient and cost-effective solutions on these areas? The paper presents the idea as an additional degree of freedom to be exploited, which can enable more efficient alternatives in the deployment of such applications. As an initial approach, the problem of the optimal positioning with respect to a set of targets of one single agent, equipped with several onboard tracking cameras with different or variable focal lengths, is addressed. As a consequence of this allowed heterogeneity in focal lengths, the notion of distance needs to be adapted into a notion of optical range, as the agent can trade longer Euclidean distances for correspondingly longer focal lengths. Moreover, the proposed optimization indices try to balance, in an optimal way, the verticality of the viewpoints along with the optical range to the targets. Under these premises, several positioning strategies are proposed and comparatively evaluated.

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A review of 28 free animal-tracking software applications: current features and limitations

Cited by 57 publications

References 52 publications

Measuring Locomotor Activity and Behavioral Aspects of Rodents Living in the Home-Cage

Measuring Locomotor Activity and Behavioral Aspects of Rodents Living in the Home-Cage

PyRAT: An Open-Source Python Library for Animal Behavior Analysis

Flying Chameleons: A New Concept for Minimum-Deployment, Multiple-Target Tracking Drones

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