## 2 Related Work  
The tracking software Tracktor uses unsupervised machine learning to track animal groups maintaining individuals' identities [13]. This software exhibited advantages in processing speed and robustness compared to the software IdTracker [14] and the ToxTrac [15]. Some other tracking software exhibit outstanding performance using deep learning algorithms [16], including the idtracker.ai [17] and the TREx [18]. These two software also apply CNNs to track many animals simultaneously with high accuracy in maintaining individuals' identities.  
In addition to tracking software, there are also tools for measuring the geometrical configuration of body parts denoted as pose estimation [16]. Deep learning approaches have also led to notable improvements in pose estimation software (e.g., DeepPoseKit,DeepLabCut, and LEAP) [19; 20; 21]. For instance, the DeepLabCut uses transfer learning with a pre-trained network in large datasets (e.g., ImageNet). This approach can improve performance and reduce the number of required training examples [19]. However, it may come with the cost of slow inference due to excessive parameterization in large networks. The LEAP framework uses a relatively simple 15 layers CNN to limit model complexity and maximize inference speed [20]. However, the LEAP achieved limited accuracy compared to the DeepPoseKit and DeepLabCut [21]. To improve the speed-accuracy tradeoff in DeepLabCut and LEAP, the DeepPoseKit toolkit was developed using Stacked DenseNet, a deep learning architecture that provides fast and accurate detection even at low spatial resolutions [21].  
The unfolding of behavioral assessments in tools without graphical user interface (GUI) (e.g., Tracktor) [13] requires familiarity with programming, which can limit the general public use. In this context, the Ethoflow software looks user-friendly due to the GUI. The available tracking software measure large collectively animal groups with high accuracy, especially those using deep learning. However, these tracking software operate by background subtraction or thresholding [13; 14; 15; 17; 18]. These approaches require video recordings of homogeneous environments and are not applicable in the field. In Ethoflow, we implemented thresholding by [PERSON]'s method [22] (Section 3.1.3) to handle assessments in a homogeneous environment. In addition, we also implemented instance segmentation by Mask R-CNN [9] for evaluation in heterogeneous environments (Section 3.1.4).  
With pose estimation toolkits, variables can be measured to predict complex animal behavior after some posterior machine learning analysis [23]. Although our goal with the Ethoflow software is tracking analysis, Ethoflow also directly measures complex behaviors. After hyperparameter optimization, we defined a parsimonious CNN architecture to assess complex binary behavior (Section 3.1.8). Deep learning software is computationally costly and requires graphics processing unit (GPU) hardware. Accordingly, they are not feasible to use on an ordinary computer. An interesting feature in our proposal is that the deep learning algorithms (used for analysis in a heterogeneous environment and measurement of complex behaviors) are separate from the other modules in Ethoflow. Wherefore, Ethoflow covers kinematic measurements on an ordinary computer and assesses more complex behavior with a GPU.