# Detection and Model of Thermal Traces Left after Aggressive Behavior of Laboratory Rodents
###### Abstract  
Automation of complex social behavior analysis of experimental animals would allow for faster, more accurate and reliable research results in many biological, pharmacological, and medical fields. However, there are behaviors that are not only difficult to detect for the computer, but also for the human observer. Here, we present an analysis of the method for identifying aggressive behavior in thermal images by detecting traces of saliva left on the animals' fur after a bite, rape attack, or grooming. We have checked the detection capabilities using simulations of social test conditions inspired by real observations and measurements. Detection of simulated traces different in size and temperature on single original frame revealed the dependence of the parameters of commonly used corner detectors (R score, ranking) on the parameters of the traces. We have also simulated temperature of saliva changes in time and proved that the detection time does not affect the correctness of the approximation of the observed process. Furthermore, tracking the dynamics of temperature changes of these traces allows to conclude about the exact moment of the aggressive action. In conclusion, the proposed algorithm together with thermal imaging provides additional data necessary to automate the analysis of social behavior in rodents. computer vision; image processing; biomedical engineering; social behavior +
Footnote †: journal: Medical  
model. The work in [14] describes the fine-tuning method of a pre-trained convolutional neural network only for the recognition of exploration behavior. [PERSON] et al. [15], in turn, present a method for behavior identification (locomotion, immobility, rearing, and grooming) using multilayer perceptron networks. The authors claim that for proper recognition of various behavior, training should be based on two types of input parameters: morphological (e.g., animal's body area or length) and kinematic (e.g., the distance traveled). They also proved that statistically-guided input feature selection significantly improves the classification performance. However, very often these machine learning-based solutions give results similar to simpler methods. This can suggest that learning-based algorithms require enriched information and it is necessary to use more various features. There are two different ways of enriching information. The first one is more circumstantial feature detection. Parameters commonly used in the training phase are body size and location, speed of movement, and mutual orientation of animals. Based on the analysis of only these features, it is possible to create system that can recognize general behaviors like grooming, exploration, chase, or approach. To discern between more complex social behaviors, e.g., bite attack or social grooming, there is, however, a need for detailed information about animals. Tracking certain regions of interest like the snout or base of the tail is rare [12; 16] but valuable for behavior recognition. The second method of enriching input information is to use various data acquisition and processing techniques and technologies. Some of the above-mentioned methods use statistics to reveal and describe sequences of behavior [8], others have extended the usual morphological feature set with kinematic [2; 15] or 3D (depth) features [13; 16]. There are also systems that use thermal technology to obtain new information [3; 17; 18]. One of the biggest advantages of thermal recordings in animal studies is undoubtedly the limitation of stress caused by artificial lighting required for recordings with visible light cameras. Many social behaviors of nocturnal animals are not possible in the strong light. Another benefit is the image of heat distribution. The value and dynamics of body temperature can visualize different physiological processes and may indicate the occurrence of behaviors such as fear or stress [19; 20]. Finally, the warmth distribution can also be an individual identifier [21].