Learning to recognize rat social behavior: Novel dataset and cross-dataset application

Lorbach, Malte; Kyriakou, Elisavet I.; Poppe, Ronald; Dam, Elsbeth A. van; Noldus, L.P.J.J.; Veltkamp, Remco C.

doi:10.1016/j.jneumeth.2017.05.006

Cited by 45 publications

(36 citation statements)

References 33 publications

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“…As most behavior studies of rats concentrate on the interaction between two rats [5][6][7][8] and cannot achieve the function of detecting and tracking multiple rats to study their social behavior, more than two rats can be detected and tracked with the framework being proposed. A Rat-YOLO tracking software is developed, which includes a friendly, real-time rat tracking platform and automatically generates labeled datasets.…”

Section: Discussionmentioning

confidence: 99%

“…As the social behavior is more complex than we can image, some works focus on the interaction between two rats [5,6], and real-time target tracking throughout the process provides a more comprehensive analysis of animal neural activity in studying animal neural activity. In the past survey of social behavior, the focus of research has been the interaction between the two rats in a cage [7,8]. Therefore, the research of social behavior with more than two rats is a good research direction to study SI in the future.The development of machine vision has been ongoing rapidly along with the development of deep learning in recent years.…”

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confidence: 99%

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A Robust Real-Time Detecting and Tracking Framework for Multiple Kinds of Unmarked Object

Dai

Chen

et al. 2019

Sensors

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A rodent real-time tracking framework is proposed to automatically detect and track multi-objects in real time and output the coordinates of each object, which combines deep learning (YOLO v3: You Only Look Once, v3), the Kalman Filter, improved Hungarian algorithm, and the nine-point position correction algorithm. A model of a Rat-YOLO is trained in our experiment. The Kalman Filter model is established in an acceleration model to predict the position of the rat in the next frame. The predicted data is used to fill the losing position of rats if the Rat-YOLO doesn't work in the current frame, and to associate the ID between the last frame and current frame. The Hungarian assigned algorithm is used to show the relationship between the objects of the last frame and the objects of the current frame and match the ID of the objects. The nine-point position correction algorithm is presented to adjust the correctness of the Rat-YOLO result and the predicted results. As the training of deep learning needs more datasets than our experiment, and it is time-consuming to process manual marking, automatic software for generating labeled datasets is proposed under a fixed scene and the labeled datasets are manually verified in term of their correctness. Besides this, in an off-line experiment, a mask is presented to remove the highlight. In this experiment, we select the 500 frames of the data as the training datasets and label these images with the automatic label generating software. A video (of 2892 frames) is tested by the trained Rat model and the accuracy of detecting all the three rats is around 72.545%, however, the Rat-YOLO combining the Kalman Filter and nine-point position correction arithmetic improved the accuracy to 95.194%.Sensors 2020, 20, 2 2 of 17 to children with autism [4]. As the social behavior is more complex than we can image, some works focus on the interaction between two rats [5,6], and real-time target tracking throughout the process provides a more comprehensive analysis of animal neural activity in studying animal neural activity. In the past survey of social behavior, the focus of research has been the interaction between the two rats in a cage [7,8]. Therefore, the research of social behavior with more than two rats is a good research direction to study SI in the future.The development of machine vision has been ongoing rapidly along with the development of deep learning in recent years. The current detection method of rats is based on the traditional image processing methods, such as background subtraction [9,10], but by this method, the background is generally set as simple, and there is a great contrast between the detected object and background in the histogram of the image. However, some social behavior requires experiments to be in different scenes, and the experimental background is dynamic rather than static; thus, the method make it more possible to recognize fault objects in the condition, Furthermore, using more complex arithmetic solves this problem which may consume more time. In c...

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Section: Discussionmentioning

confidence: 99%

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confidence: 99%

A Robust Real-Time Detecting and Tracking Framework for Multiple Kinds of Unmarked Object

Dai

Chen

et al. 2019

Sensors

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“…To classify rodent interactions, we use a log-linear classification model. The model can be learned efficiently, has only one free parameter and has performed sufficiently well in previous rodent behavior experiments [28,29]. The model allows for the calculation of a confidence score by using a suitable loss function, as we will explain shortly.…”

Section: Fig 2 Framework Components See Text For Detailsmentioning

confidence: 99%

“…Natural rodent behavior exhibits a high degree of variability across occurrences and subjects [6]. Annotation tools with pre-trained classifiers are therefore limited to a specific range of settings reflected by the training data [28]. Outside this scope, for example when new behavior classes are added or when the behaviors are performed differently due to disease progress [44], treatment or genetic background [38], the researchers need to be able to quickly train or re-train classifiers.…”

Section: Introductionmentioning

confidence: 99%

“…These are the standard components in active learning applications across several domains. In this work we implement 1 and apply them to annotate rodent social behavior in two public datasets, RatSI [28] and CRIM13 [8], as well as a third validation dataset with young rats (YR). We demonstrate the merits of our approach in labeling a full dataset with a significant speed-up.…”

Section: Introductionmentioning

confidence: 99%

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Interactive rodent behavior annotation in video using active learning

Lorbach

Poppe

Veltkamp

2019

Multimed Tools Appl

Self Cite

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Manual annotation of rodent behaviors in video is time-consuming. By learning a classifier, we can automate the labeling process. Still, this strategy requires a sufficient number of labeled examples. Moreover, we need to train new classifiers when there is a change in the set of behaviors that we consider or in the manifestation of these behaviors in video. Consequently, there is a need for an efficient way to annotate rodent behaviors. In this paper we introduce a framework for interactive behavior annotation in video based on active learning. By putting a human in the loop, we alternate between learning and labeling. We apply the framework to three rodent behavior datasets and show that we can train accurate behavior classifiers with a strongly reduced number of labeled samples. We confirm the efficacy of the tool in a user study demonstrating that interactive annotation facilitates efficient, high-quality behavior measurements in practice.

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The Neuropharmacology of Impulsive Behaviour, an Update

Pattij

Vanderschuren

2020

Recent Advances in Research on Impulsivity and Impulsive Behaviors

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Learning to recognize rat social behavior: Novel dataset and cross-dataset application

Cited by 45 publications

References 33 publications

A Robust Real-Time Detecting and Tracking Framework for Multiple Kinds of Unmarked Object

A Robust Real-Time Detecting and Tracking Framework for Multiple Kinds of Unmarked Object

Interactive rodent behavior annotation in video using active learning

The Neuropharmacology of Impulsive Behaviour, an Update

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