This paper introduces a dynamic model for multi-target tracking that incorporates social behavior, improving tracking performance in crowded scenarios. The model, called Linear Trajectory Avoidance (LTA), accounts for interactions between pedestrians and their intended destinations, allowing for more accurate predictions and better handling of occlusions. Unlike traditional models that rely solely on past motion data, LTA considers future intentions and environmental factors, leading to more realistic and effective tracking. The model is trained using bird's-eye view data and can be applied to various tracking scenarios, including those from a moving camera. It operates in physical world coordinates and is designed to handle short-term predictions, which are crucial for avoiding collisions and maintaining smooth motion. The model's parameters are learned from training sequences, and it has been shown to outperform traditional models in terms of tracking accuracy, especially in complex and crowded environments. Experiments demonstrate that LTA significantly improves tracking performance compared to models that ignore social interactions. The model's ability to predict future movements and adjust trajectories in real-time helps in avoiding collisions and maintaining accurate tracking even when objects are occluded. The results show that LTA reduces tracking errors and improves the accuracy of data association, particularly in scenarios with frequent occlusions and low frame rates. The paper also highlights the importance of considering destination information in tracking, as it provides valuable insights that help in predicting future movements. This is especially relevant in cases where continuous observation is not possible, such as with mobile cameras. The study emphasizes that even approximate destination information can enhance tracking performance, making the model more robust and effective in real-world applications. Overall, the LTA model represents a significant advancement in multi-target tracking by incorporating social behavior and environmental context into the dynamic model.This paper introduces a dynamic model for multi-target tracking that incorporates social behavior, improving tracking performance in crowded scenarios. The model, called Linear Trajectory Avoidance (LTA), accounts for interactions between pedestrians and their intended destinations, allowing for more accurate predictions and better handling of occlusions. Unlike traditional models that rely solely on past motion data, LTA considers future intentions and environmental factors, leading to more realistic and effective tracking. The model is trained using bird's-eye view data and can be applied to various tracking scenarios, including those from a moving camera. It operates in physical world coordinates and is designed to handle short-term predictions, which are crucial for avoiding collisions and maintaining smooth motion. The model's parameters are learned from training sequences, and it has been shown to outperform traditional models in terms of tracking accuracy, especially in complex and crowded environments. Experiments demonstrate that LTA significantly improves tracking performance compared to models that ignore social interactions. The model's ability to predict future movements and adjust trajectories in real-time helps in avoiding collisions and maintaining accurate tracking even when objects are occluded. The results show that LTA reduces tracking errors and improves the accuracy of data association, particularly in scenarios with frequent occlusions and low frame rates. The paper also highlights the importance of considering destination information in tracking, as it provides valuable insights that help in predicting future movements. This is especially relevant in cases where continuous observation is not possible, such as with mobile cameras. The study emphasizes that even approximate destination information can enhance tracking performance, making the model more robust and effective in real-world applications. Overall, the LTA model represents a significant advancement in multi-target tracking by incorporating social behavior and environmental context into the dynamic model.