EDDSN-MRT: multiple rodent tracking based on ear detection and dual siamese network for rodent social behavior analysis

<jats:title>Abstract</jats:title><jats:sec>                 <jats:title>Background</jats:title>                 <jats:p>Rodent social behavior is a commonly used preclinical model to interrogate the mechanisms underpinning various human neurological conditions. To investigate the interplay between neural systems and social behaviors, neuroscientists need a precise quantitative measure for multi-rodent tracking and behavior assessment in laboratory settings. However, identifying individual differences across multiple rodents due to visual occlusion precludes the generation of stable individual tracks across time.</jats:p>               </jats:sec><jats:sec>                 <jats:title>Methods</jats:title>                 <jats:p>To overcome the present limitations of multi-rodent tracking, we have developed an Ear Detection and Dual Siamese Network for Multiple Rodent Tracking (EDDSN-MRT). The aim of this study is to validate the EDDSN-MRT system in mice using a publicly available dataset and compare it with several current state-of-the-art methods for behavioral assessment. To demonstrate its application and effectiveness in the assessment of multi-rodent social behavior, we implemented an intermittent fasting intervention experiment on 4 groups of mice (each group is with different ages and fasting status and contains 8 individuals). We used the EDDSN-MRT system to track multiple mice simultaneously and for the identification and analysis of individual differences in rodent social behavior and compared our proposed method with Toxtrac and idtracker.ai.</jats:p>               </jats:sec><jats:sec>                 <jats:title>Results</jats:title>                 <jats:p>The locomotion behavior of up to 4 mice can be tracked simultaneously using the EDDSN-MRT system. Unexpectedly, we found intermittent fasting led to a decrease in the spatial distribution of the mice, contrasting with previous findings. Furthermore, we show that the EDDSN-MRT system can be used to analyze the social behavior of multiple mice of different ages and fasting status and provide data on locomotion behavior across multiple mice simultaneously.</jats:p>               </jats:sec><jats:sec>                 <jats:title>Conclusions</jats:title>                 <jats:p>Compared with several state-of-the-art methods, the EDDSN-MRT system provided better tracking performance according to Multiple Object Tracking Accuracy (MOTA) and ID Correct Rate (ICR). External experimental validation suggests that the EDDSN-MRT system has sensitivity to distinguish the behaviors of mice on different intermittent fasting regimens. The EDDSN-MRT system code is freely available here: <jats:ext-link xmlns:xlink="http://www.w3.org/1999/xlink" ext-link-type="uri" xlink:href="https://github.com/fliessen/EDDSN-MRT">https://github.com/fliessen/EDDSN-MRT</jats:ext-link>.</jats:p>               </jats:sec>

section_content: To deal with the occlusion interference problem, based on the nature physiological structure of rodents, we propose a Multiple Rodent Tracking approach based on Ear Detection and Dual Siamese Network for tracking of multiple rodents in laboratory environments and analysis of rodent social behavior, named EDDSN-MRT. We validate this tracking system on a public dataset with multiple rodents and compare it with several current state-of-the-art methods. At least 4 the main procedure is distinguishing video frames by the presence or absence of occlusion and then tracking rodents in occlusion and non-occlusion frames respectively. The main challenge of multiple rodent tracking is how to assign rodents with correct identities after they touch, cross or they are occluded by each other (i. e., the occlusion condition). 

Previous studies address the occlusion interference problem in multiple rodent tracking mainly from three aspects. The first aspect is using a kind of social behavior models without physical contact of individuals such as the three-chamber social test [8, 9, 10]. In this kind of models, rodent individuals are completely isolated in spatial from each other. Thus, touching and crossing between individuals are eliminated in the video. However, because of the limitation of moving areas, these approaches have no ability to offer the comprehensive investigation of trajectories of spontaneous and free-moving mice. The second one is applying bio-loggers (or labels and tags) or special devices, such as the radio frequency identification (RFID) or multiple cameras [11, 12, 13], during data recording. With the aid of special equipment, these approaches achieve a high accuracy of tracking. However, attaching or implanting sensors into rodents is considered as a disadvantage. It leads to the high cost in devices and the more complex operations. 

Besides, the fact that rodents carry these sensors or tags is also a negative factor, it affects their routine behavioral trajectories. The last aspect is using end-to-end methods based on monocular videos of single view. This is a kind of state-of-the-art methods in Multiple Object Tracking (MOT) research field and has widely used in tracking of pedestrians [14], vehicles [15], and ani mals [16, 17] in complex environments. However, because of the lacking in labeled dataset, the similarity in individuals" appearances and a wide range of shapes, these approaches lead to undesirable accuracy (usually rely on manual calibration) and high time-consuming in a long period tracking even that the video stream recorded in laboratory open field test (OFT) with a featureless circular background. 

To overcome the shortcomings of the current approaches, this study proposes a framework based on Ear Detection and Dual-Siamese Network for Multiple Rodent Tracking in laboratory environments--EDDSN-MRT with the ability to address the challenge in the tracking of occlusion frames (shown in Fig. 1 ). We divide frames into occlusion frames and non-occlusion ones via a segmentation process, and then implement tracking operations respectively. Before tracking in occlusion frames, we select rodent ears as the key points for tracking which are with the lower possibility to occur occlusion with each other in fragments with individuals" touching and crossing. 

These ears" images are used as input instead of the entire body of rodents to train an ear detection network (EDN) to locate and recognize the rodents" ears, which is helpful for extracting ears" spatial (position) information and image characteristics. Besides, we propose a dual-Siamese network used spatial information and image characteristic of the detected ears as input to calculate the similarity between two frames that is used to assign identification of each rodent. With this network, similarity calculating and ID assigning in occlusion fragments are done. We test our proposed EDDSN-MRT on a public mice dataset. By comparing the results with some state-of-art methods [17, 18], it shows that our framework can complete tracking operation in occlusion fragments and present better performance. In addition, we validate our framework on a private dataset, which shows it can contribute to the behavior analysis, including locomotion characterizing, movement phenotyping, and group level location distributing profiling.
section_name: Results

section_content: In the following sections, we demonstrate the advantage of the EDDN-MRT to tracking multiple rodents. First, we describe the complete pipeline of tracking framework. And then, we provide experimental evidence including the advantage of improved ear detection network by compared with several object detection methods, the advantage of EDDN-MRT in public multiple rodent tracking dataset by compared with several existing state-of-the-art animal tracking methods, as well as the verification of ablation study. Finally, we introduce the usage of EDDN-MRT on clinical locomotion dataset.
section_name: Results

section_content: As shown in Fig. 1, we divide frames into occlusion frames and non-occlusion ones via a segmentation process, and then implement tracking operations respectively. Before tracking in occlusion frames, we select rodent ears as the key feature for tracking which are with the lower possibility to occur occlusion with each other in fragments with individuals" touching and crossing. 

These ears" images are used as input instead of the entire body of rodents to train an improved ear detection network (EDN) based on Path Aggregation Network (PANet) [19] to locate and recognize the rodents" ears, which is helpful for extracting ears" spatial (position) information and image characteristics. Besides, we propose a dual-Siamese network used spatial information and image characteristic of the detected ears as input to calculate the similarity between two frames that is used to assign identification of each rodent. With this network, similarity calculating and ID assigning in occlusion fragments are done.
section_name: Pipeline of EDDN-MRT

section_content: As shown as Table 1, we evaluate our proposed ear detection network on the testing set of Dataset A and compare results to several state-of-art object detection methods. Dataset A contains a free-roaming mouse in the OFT (open filed test) experimental environment, so it is very suitable for training and testing ear detection. 

It is observed that our EDN increases mAP@ 0. 5:0. 9 (mean average precision at IoU is 0. 5 and from 0. 5 to 0. 9) by 12. 03% compared with Efficient Det [20] (52. 29%), showing the effectiveness of data argumentation and adaptive anchor box functions. Due to the extreme size of subjects in Dataset A (about 5~8 pixels), these two functions exert more significant impact on performance comparing with being applied on the other public datasets. But the difference is smaller in mAP@0. 5 (mean average precision at IoU is 0. 5, 97. 07% vs 95. 41%). Maybe the metric index of the original network is already too high to obviously improve the performance. In other aspects, by comparing the results of YOLOv5 [21] and EDN proposed in this study, it indicates that our new EDN has relatively higher mAP@0. 5 (97. 07% vs 96. 15%) and significantly higher mAP@0. 5:0. 9 (64. 32% vs 59. 76%,). As mentioned above, the small difference on mAP@0. 5 may be caused by the fact that the performance of original framework is already very high and very difficult to be outperformed. The difference between the new and the old network is up to 4% on mAP@0. 5:0. 9. It presents that the EDN with new Neck and Head model achieves a better performance in spatial position capturing. Video B5 and B6 are different from the previous videos, they contain 4 mice respectively. 

The idtrackerai and our EDDSN-MRT can be run on these two videos. Due to the occlusion caused by manual operations for a period in B6, we cropped B6 into two segments when using EDDSN-MRT. However, Toxtrac cannot be implemented on the B6, this led to the missing of results of Toxtrac on these two videos (as Tabel. 2 shows). Our method obtains results like the ones of above four videos. It still outputs the result with no ID missing, sporadic ID switching and more ID drift (comparing with the numbers of ID missing and ID switching). It indicates that the performance of our method has not degraded in this kind of video (with more individual and longer time duration). The performance of Toxtrac and idtracker. ai are further degraded. It is worth mentioning that the number of ID drift errors in the results of idtracker. ai has been reduced to 0 at this time (similar to the results of Toxtrac). However, there are still a lot of ID drift errors in EDDSN-MRT. As in the previous analysis, the number of ID drift errors dropped to 0 does not mean better tracking performance, but because the detection performance is so poor that most video frames cannot even enter the stage of ID tracking. 

If we observe the two-evaluation metrics from a global perspective, we will find that MOTA is insensitive to ID switching errors. The fact that only a few switches occur but the mice hold the wrong ID for a long period of time does not significantly reduce the MOTA assessment.
section_name: Results for ear detection network

section_content: To verify the effectiveness of each component in EDDSN-MRT, we design ablation experiments on the video B1. The results are summarized in Table 3.
section_name: Ablation study

section_content: To show the effectiveness of ear detection-based method (EDB), we perform an ablation study where implements tracking with traditional torso detection-based method (called TDB). 

From Table 3, TBD + IEDN + DSN indicates that it uses torso of rodents as the target to implement object detection (Torso Based method, TDB) and do tracking operation consequently. 

Under the same backbone network, the method using ear as targets gets 99. 58% correct ID rate, significantly higher than that of TBD (. This verifies the effectiveness of the EDB.
section_name: Effect of Ear Detection Based method (EDB)

section_content: To show the effectiveness of improved ear detection network (IEDN), we perform an ablation study where implements ear detection with original PANet (called EDN). The second and fourth row of Table 3 show that improved PANet can significantly improve ICR from 98. 2% to 99. 58%. 

Combined with the data shown in Table 2, it is obvious that the object detection framework using the enhanced PANet has a better ability to locate targets (improve mAP0. 5:0. 9 from 58. 58% to 64. 32%), which makes this method suitable for environment. Above analyses show that the IEDN is effective for both rodent detection and tracking.
section_name: Effect of Improved Ear Detection Network (IEDN)

section_content: The dual-Siamese network used in this study is a framework with two independent Siamese networks, in which one of them is used to process image information of rodents" individuals and the other one is used to preserve spatial information. To show the effectiveness of it, we perform an ablation study where uses traditional Single-Siamese network (SSN). We compare it with SSN which only processes images to validate the effectiveness of DSN. The ICR of DSN is 32. 17% higher than the one of SSN (see Table 3 ). The reason may be that the area of the mouse ear is very small, even in 1920×1080 resolution, it is still only with 30×30 (pixels) size. It is difficult to only use image features alone for tracking without using spatial information for constraints. These validate the necessity of DSN and also show the presence or absence of spatial information has a huge impact on the performance of the entire tracking framework.
section_name: Effect of Dual Siamese Network (DSN)

section_name: Results of clinical validation

section_content: We monitor the movement of the 32 animals and obtain 32 tracking trajectories and the variation curve of average velocities by block of 5min (video is 40min in total). Compared with the single-session experiment, the group analysis reveals diverse locomotion characteristics. 

According to [22, 23], as individual age, the damaged mitochondria would produce less adenosine triphosphate (ATP) and more reactive oxygen species (ROS) would accumulate, which would result in depression symptoms and in turn lead to the weakening of locomotion ability. This is also observed in the results of this experiment. As shown in Fig. 2, the old mice (groups of mice with 18-mounth age) are with the lower average velocity in both the AL (Ad Libitum feeding) and the IF (Intermittent Fasting) groups. Comparing with 18-month mice, 3-month mice with the same feed supplement have more frequency of ambulation. And based on previous research [24], IF intervention is believed to help to alleviate depressive symptoms and thus would improve the locomotor performance and the range of motion of animals. 

Thus, as we can see in Fig. 2, it can be found that in the group of same age, the intermittent fasting intervened (IF) mice are with significantly higher average velocities. This is consistent with the results of [40] - [24]. By manually reviewing the relation curve of the velocity difference between IF/AL groups in the same time frame, we find that the maximum difference in locomotion intensity appears in the period from 20min to 25min (Fig. 3
section_name: Results of velocity

section_content: As shown in Fig. In previous studies, the locomotion in OFT illustrates depression and anxiety symptoms of mice [25, 26, 27, 28] and IF intervention should be a method to relieve these symptoms [28, 29, 30],

and which in turn leads to increase of dwelling ranges. However, according to our observations, in both age groups, the AL mice have a larger range of locomotion than the IF ones. So, the results of mouse dwelling distribution do disagree with the conclusions of previous articles [44, 45, 46] - [28, 29, 30]. There is no evidence of social behavior showing that fasting interventions or reliving of depression symptoms would lead mice to clustered social behavior. This phenomenon indicates that IF-intervention may exert a hidden influence on social behavior of mice instead of simply relive mental symptoms or affect mood. This novel finding can be attributed to that our framework can obtain comprehensive locomotion and spatial distribution analysis from multiple samples in groups instead of indicators from a single session.
section_name: Results of dwelling distribution

section_content: Inspired by the natural physiological structure of rodents, this study presents an approach for detecting ears of mice instead of the whole body to avoid occlusion interference in multiple object detection. The approach makes multiple rodent tracking based on object detection to be accessible. 

And to adapt to detecting ears with extremely small sizes, we improve the PANet structure and let it obtain more detail features from the low-level layers. In the conventional architectures of ODN (object detecting network), PANet is an independent component for feature extracting. Therefore, this improvement could be applied to most current ODN similar to Yolo. It is feasible to be adapted on a new ODN with the better performance in future. Spatial and image information are extracted by ODN. We use a dual-Siamese-network to measure the similarity between a pair of ears" images and spatial information in adjacent frames to determine identification assignment. 

Comprehensive and unbiased locomotion phenotyping is becoming a powerful approach in the research of behavioral abnormalities in animal models of social behavioral disorders. In this study, it demonstrates its application in the monitoring of social behavior of intermittent fasting mice with different ages. Our framework reveals that mice with IF intervention are significantly more active in spontaneous moving than the AL mice, particularly in 20 to 25 min time frame. 

Previous studies [44, 45, 46 ] - [28, 29, 30] mentioned that the IF would intervene rodents" moods and intervention not only has a significant impact on the mood of mice, but also on their physiologic function. 

Lastly, we would like to discuss the limitations of our framework. The ear of mice is a kind of biometrical characteristic (BMC), but the BMC tracking performance heavily depends on the ODN designed for the special feature. While the feature of BMC is unreliable because of various problems, this framework would be totally unworkable. For example, BMC in this article is the ear of mice. But not all types of rodents have such distinct appearance of ear, some types of rats with white hair have very slight difference between hair and ears' color. In this case, we need to select a new BMC for tracking. If the solution is not ideal, the tracking performance would be unsatisfactory. As a next step, we are considering using generative adversarial network or semantic image segmentation to generate visible BMC mark for rodent subjects to enhance performance of ODN. Solving these problems will extend the applicability of our framework to the benefit of the animal behavioral research community.
section_name: Discussion

section_content: The EDDSN-MRT is an automated pipeline framework for multiple rodent tracking 1 The dataset which the video clip belongs to. 2 The number of subjects contained in this clip. 3 Physilogical interventions applied to subjects. 4 Frames per second, frame rate of the video recording. 

Dataset A
section_name: Conclusion

section_content: This public dataset contains 6 clips of video used in validation of tracking system performance. The video streams in this dataset are obtained via the resource of [17]. Two videos with four mice are recoded inside a translucent plastic cage of size 30 × 47 × 35cm³ (length × width × height) chamber inside a bigger tank made of glass. No roof, the walls are high enough to prevent the mice from escaping. Four videos with two mice are recorded in a transparent plastic cage of size 18 × 32 × 20 cm³ covered with a transparent perspex roof to prevent the mice from escaping. In both cases, the bottom of the cage is covered with sawdust for comfort of the animals. 

Cameras are set around 110cm and 100cm from the top of the ground for the four-mice and two-mice videos, respectively. Except that the individual in video named 2aguties is agouti, the rest are presumed to be C57BL mice. Details of the dataset B are shown in Table 4.
section_name: Dataset B

section_content: This private dataset contains 4 clips of video used in clinical movement monitoring t esting.
section_name: Dataset C

section_content: Like most conventional multiple animal tracking approaches, we divide frames into occlusion frames and non-occlusion ones in preprocessing section. The first step is segmentation [39, 40],

given a frame of video, it is required to distinguish between pixels associated to subjects or background. In this step, each frame is normalized with respect to its average intensity to correct for illumination fluctuations. It is also possible to implement background subtraction by generating a model of the background calculated as the average of a collection of frames obtained via subsampling the video. And according to the standard notation adopted in computer vision, we refer to a collection of connected pixels which is not part of the background as a blob. The second step is frame classification, frames are divided into occlusion frames and non-occlusion frames in this step. In open field test, the number of rodent subjects is a constant value declared by user. It is possible to perform a comparison between the number of calculating and the number of actual experiment subjects to distinguish if the frame occurring occlusions. In simply, when the number of blobs in a frame corresponding to the actual one of subjects, we consider this frame as a non-occlusion frame. And if they are not same, we consider it as an occlusion frame.
section_name: Data processing

section_content: In non-occlusion frames, the individuals are not occurring occlusion by default. Thus, one blob can be used to present one individual. In this case, blob data can be used to generate continuous individual trajectories that track the motion of rodents. As we know in a video with the high frame rate, the location in spatial of a rodent does not change too much in the gap of two adjacent frames. So, if we overlay two adjacent frames into one image, the pair of blobs presenting the same individual would share large number of pixels in spatial. So, in adjacent frames, the blob with the most overlapping pixels inherits the identification of the blob in the previous frame and the identifications can be assigned for every blob frame-by-frame. 

Technologies in the branch of non-occlusion tracking are simple and proven [41].
section_name: Tracking in non-occlusion frames

section_content: Because of the occlusion of subjects, some blobs in occlusion fragments would contain multiple individuals in spatial. So, we cannot assign identification directly like the operation in non-occlusion frames. In this study, the tracking operation in occlusion frames consists of three main steps. The first step is ear labeling, the ear is used for tracking in occlusion frames instead of whole body of individual. And then, this object detection network would be used to extract ears in occlusion frames. The last step is using a dual-Siamese network to assign identification.
section_name: Tracking in occlusion frames

section_name: Ear labelling Since the video in

section_content: Because of the extreme small sizes of rodent ears, the conventional detection network which lacks interpretability of extreme small size objects, and results in a low accuracy. In this case, improvement for detecting small size objects is indispensable. In this step, we apply YOLOv5 [21] as the prototype framework for its flexibility in modification and improve it. The structure of this kind of networks is shown as (2) Neck: a variant of Path Aggregation Network (PANet) [43], and (3) Head: Yolo Layer. The images are first input to the Backbone for feature extraction, and then fed to PANet for feature fusion. Finally, the Head outputs detection results. 

Similar to other methods in the same field, transfer learning methods using pre -trained models can shorten the training time and improve accuracy. Here we tested the performance of the YOLOv5 models with and without pre-trained weights, and the results are shown in Table 5. It can be seen that on mAP@0. 5 and mAP@0. 5:0. 9 (mean average precision at IoU is 0. 5 and from 0. 5 to 0. 9), the performance of the models with pre-trained weights are relatively better.
section_name: Ear detection network

section_content: Model (Large CSPDarknet as backbone) mAP@0. 5 mAP@0. 5:0. 9 for rodent identification assignment, in the Siamese network for processing images (the input 1), we used ResNet50 [44] as architecture and Cosine Embedding loss [45] to learn the association metric of images. In the network for processing spatial information (the input 2), the Margin Ranking Loss [46] is used to measure similarity score of coordinates of two ears.
section_name: TABLE 5: Performance difference between pretrained and non-pretrained models

section_content: Generally, the final tracking result is a combination of the results in occlusion frames and non-occlusion frames. The key to the combination is to link the tracking trajectories in both kinds of frames. Here, we use a frame-classifying operation to make every occlusion fragment to incorporate one previous frame and one following frame (these frames are non-occlusion frames). 

And then, these frames are employed for tracking using both strategies (the one for occlusion and the one for non-occlusion frames) and are assigned with the same identification to link the trajectories in two kinds of fragments.
section_name: Generation of the final tracking results

section_name: Experiments

section_name: Implementation details

section_content: The and the number of epochs is 50, the batch size is set to 8 and the learning rate is set to 0. 01. The mainhyper-parameters of ear detection network are shown in Table 6. 

There are 4 different pre-trained CSPdarknet models on MS-COCO [47] dataset ranging from the smallest one with 7. 5 million parameters and 140 layers to the largest with 89 million parameters and 284 layers. From Table 7 showing ear detection performance of these 4 pre-trained models, we can find that the "Large" pre-trained model achieves the highest mAP@0. 5:0. 9 (mean average precision from 0. 5 to 0. 9 interaction over union), thus, in this study, we use the "Large" pretrained model.
section_name: Implementation details for Ear detection network

section_content: Since dataset A is currently the only accessible dataset to do automatic labeling, this dual-Siamese-network has to be trained with the ear images and spatial information extracted from The left two non-interfered fragments (with 37483 and 36603 frames respective) are processed using our method respectively.
section_name: Implementation details for training and testing with dual-Siamese network

section_content: Intermittent fasting (IF) is an increasingly popular dietary approach used for weight management and maintenance of overall health [48]. Preclinical studies consistently show the robust disease-modifying efficacy of intermittent fasting in animal models on a wide range of chronic disorders, including obesity, diabetes, cardiovascular disease, cancers, and neurodegenerative brain diseases [49, 50, 51, 52, 53]. Tracking trajectories' analysis provides a noninvasive approach to the assessment of locomotion disorders in animal models. We collect data from 32 mice (n 18m-IF = 8, n 18m-AL = 8, n 3m-IF = 8, n 3m-AL = 8, as shown in Table 4 ) in Dataset C with our tracking system and subjected them to distributions of temporal features (eg, velocity)

analyses. Clips in Dataset C are used with original parameters and resolutions. By only evaluating spontaneous movement without any induced conditions, we demonstrate the usability and unbiased character of our framework for animal individual and social behavior monitoring. We subject mice to distributions of temporal features (eg, velocity) analysis. By applying tracking system in this experiment, differences in average velocities and location distribution between the IF and AL groups would be observed. We can investigate the influence of IF on locomotion.
section_name: Implementation details for clinical validation on intermittent fasting intervention

section_name: Methods for comparison

section_content: To show the effectiveness of the proposed ear detection network, we compare it with several object detection methods as follows:

(1) YOLOv3

YOLOv3 is the 3 rd version of YOLO series [42]. It employs multi-scale schema, predicting bounding boxes at different scales. This allows Yolov3 to be more effective for detecting smaller targets when compared to previous version Yolos. It uses dimension clusters as anchor boxes in order to predict bounding boxes around the desired objects in given images. Logistic regression is used to predict the objectness score for a given bounding box. 

Here, it was trained with Adam optimizer with learning rate 0. 001, number of epoch is set to 50, batch size is set to 8, resolution = 1280×736 (YOLOv3 network only accepts resolutions whose value is an integer multiple of 32), momentum = 0. 9. 

( network [54] used to be against object deformation. 

It was trained with SGD optimizer with learning rate 0. 01, epoch is set to 50, batchsize is set to 8, resolution = 1280×720, momentum = 0. 937. 

(
section_name: Ear Detection methods

section_content: The EfficientDet is an object detection framework built by Google Brain team [20]. It achieved state-of-the-art accuracy on the popular MS-COCO dataset [47]. It includes pre-trained models classed from D0 to D7, they have different numbers of parameters (D0 with the fewest and D7 with the highest). 

In the application purpose considered (for video frame processing, there is the requirement of execution speed), the EfficientDet D1 is selected. It was trained with SGS optimizer with 0. 00005 learning rate, epoch is set to 50, batchsize is set to 8, momentum is set to 0. 9.
section_name: 3) EfficientDet

section_content: To show the performance of the proposed EDDSN-MRT, we compare it with several existing state-of-the-art animal tracking methods as follows:

(1) Toxtrac Toxtrac [18] is an automated open-source executable software for image-based tracking that can simultaneously handle several organisms monitoring in a laboratory environment. It can be used for high-speed tracking of insects, fish, rodents or other species to provide useful locomotor information in animal behavior experiments. 

It was implemented with threshold set to 90, minimum Object size set to 2000, maximum Object size set to 40000, maximum Distance/Frame set to 100, the numbers of individuals corresponded to the number of mice in the video. 

(2) idtracker. ai Idtracker. ai [17] is an image-based multi-animal tracking system that uses convolutional neural networks to identify each of the individuals in the video. It uses offline training strategy. In the videos with a higher density of individuals, idtracker. ai extracts frames of the single individuals to train an image classification network to identify individuals. 

It was implemented with area is set as [2000, 4000], intensity is set as 80. The number of blobs is set equal to the number of individuals of each video. The range is set equal to the number of frames of each video. 

identifies. The direct output of the tracker is a series of IDs, which are mapped to our manually-annotated tracks. The result of this implementation is a large number of ""tracklets"" (partial tracks), subsets of which belong to individual identities. 

The rodent tracking is still in its infancy, and research in this field is limited. So it is difficult to use the existing multi-object tracking (MOT) metrics to illustrate performance of approaches. 

For this reason, this paper also introduces the metric ICR (ID Correct Rate). The ICR means the number of images correctly identified over the total number of individual images validated. It was used in [17] and [18] (as following equation). where the missing identities (Miss), the switched identities (Switch) and the drifted identities (Drift) are the three types of errors that occur. Via the mapping between output and manually-annotated tracks, it can be identified when the tracker is not able to detect an object (missing identities), when the tracker detects an object with wrong position (drifting identities), or when the identities (two or more) tracks are switched. 

It needs to emphasize that these methods are designed based on the constant number of experiment rodents. This designing strategy would limit the tracker to provide individuals trajectories more than the number experimental rodents.
section_name: Animal tracking methods

section_content: Ethics approval and consent to participate All animal procedure were in strict accordance with the guidelines of animal welfare set by the World Organization for Animal Health and approved by the Central South University, Xiangya
section_name: Declaration

section_name: Acknowledgement

section_content: We thank Dr. Hulin Kuang for the supports in article writing and revision.
section_name: Acknowledgement

section_content: Funding This work was supported by the National Key Research and Development Program of China (No. 2021YFF1201200 ), the Hunan Provincial Science and Technology Innovation Leading Plan (No. 2020GK2019 ), the 111 Project (No. B18059 ), and the National Natural Science Foundation of China (No. 62102454 ).

section_name: Availability of data and material

section_content: Rodent public dataset (Dataset B) is free-available and can be download through (https://idtrackerai. readthedocs. io/en/latest/data. html). The rest private datasets (Dataset A and C) that support the findings of this study are available from Xiangya School of Medicine, Central South University but restrictions apply to the availability of these data, which were used under license for the current study, and so are not publicly available. Dataset A and C are however available from the authors upon reasonable request and with permission of Xiangya School of Medicine, Central South University.
section_name: Availability of data and material

section_name: Method Datasets

section_content: with the current approaches, our proposed EDDSN-MRT achieves better performance in identification assignment for tracking rodent individuals. It also helped us to discover the unexplained effects of intermittent fasting on rodent behavior in the clinic. Evaluation metrics

section_content: As the methods for many conventional object detection networks, the mean average precision--mAP@0. 5 and mAP@0. 5:0. 9 are introduced as evaluation metrics to quantitatively measure the detecting performance. These evaluation metrics are based on the Intersection over Union (IOU) of the ground truth and detected bounding boxes.
section_name: Metrics for ear detection

section_content: We set the threshold to determine whether the object is a true positive. mAP@0. 5 means when IoU is set to 0. 5, the average precision of all categories is calculated independently and then averaged by the number of categories. In addition, mAP@0. 5:0. 9 illustrates the average mAP over different IoU thresholds (from 0. 5 to 0. 9, in steps of 0. 1).
section_name: 𝐼𝑜𝑈 = 𝐴𝑟𝑒𝑎 𝑜𝑓 𝑜𝑣𝑒𝑟𝑙𝑎𝑝 𝑏𝑒𝑡𝑤𝑒𝑒𝑛 𝑏𝑜𝑢𝑛𝑑𝑖𝑛𝑔 𝑏𝑜𝑥𝑒𝑠 𝐴𝑟𝑒𝑎 𝑜𝑓 𝑢𝑛𝑖𝑜𝑛 𝑏𝑒𝑡𝑤𝑒𝑒𝑛 𝑏𝑜𝑢𝑛𝑑𝑖𝑛𝑔 𝑏𝑜𝑥𝑒𝑠

section_content: We use the widely accepted metric multi-object tracker accuracy (MOTA) proposed in the 2016 MOT Challenge [55]. To evaluate the performance of trackers, we use the py-motmetrics library. The MOTA tracking performance measure used in this study is the most commonly used metric to benchmark MOT solutions (as following equation). 

where false negative (FN), false positive (FP) and identity switch (IDSW) are the three types of errors that occur. False negatives are defined as an object that is not tracked, false positives are defined as tracked objects which should not be, and identity switches are when two objects that should be tracked swap identities. The GT indicates the ground truth number of individuals School of Medicine. No human participants, tissue, or data were used in this study.
section_name: Metrics for multiple rodent tracking

section_content: All authors consent to the publication of the manuscript
section_name: Consent for publication

section_content: The authors declare that they have no competing interests. 

Authors" contributions BL made substantial contributions to the conception and design of the work, acquisition and analysis of the data, and the creation of new software used in the work, and BL was a major contributor in writing the manuscript. YQ made substantial contributions to the conception of the work, and acquisition and interpretation of the data. JW made substantial contributions to the conception and design of the work, analysis and interpretation of the data， and manuscript revision. 

All authors read and approved the final manuscript.
section_name: Competing interests

section_content: This is a list of supplementary les associated with this preprint. Click to download. 

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section_name: Supplementary Files