Bayesian Tracking

Bayesian tracking aims to estimate the trajectory of objects over time by probabilistically integrating noisy measurements from various sources, such as radar, lidar, or cameras. Current research emphasizes improving accuracy and efficiency through advanced model architectures, including Kalman filters, particle filters, and deep learning approaches like transformers and neural networks, often within a multi-object tracking framework. These advancements are driving progress in diverse applications, from autonomous driving and robotics to biological studies requiring precise 3D tracking of numerous dynamic objects. The integration of deep learning with probabilistic methods is a particularly active area, aiming to leverage the strengths of both paradigms for robust and accurate tracking.