Modern Methods for Dynamics and Trajectory Tracking Modeling of High-Degree-of-Freedom Systems

Abstract

The study is devoted to analyzing a key methodological shift in the quantitative assessment of bio-object behavior: the transition from classifying individual behavioral acts to analyzing their dynamic structure. The problem statement lies in the existence of a methodological gap: traditional tracking methods simplify behavior to the center-of-mass trajectory, while modern deep learning algorithms, providing high pose recognition accuracy, often ignore the temporal context and function as computationally expensive "black boxes". The aim of the work is to analyze existing approaches to video processing and substantiate a hybrid methodology that combines the detail of neural network analysis with a systemic approach to dynamics. The objectives include reviewing methods ranging from manual annotation to transformer architectures and identifying their limitations in long-term forecasting tasks. The research methods are based on a comparative analysis of computer vision algorithms, deep learning, and stochastic modeling in the context of processing video data from laboratory animal experiments. The main results show that neural networks are optimized primarily for local classification. The paper proposes a methodological framework integrating frame-level classification with the construction of interpretable stochastic models (transition probability matrices). The main conclusions indicate that using Markov representations in discrete state spaces allows for the effective identification of stable behavioral modes (attractors) and anomalies, creating a reliable basis for decision support systems without excessive computational requirements.