Kalman Filter Source Code for Video Sequence Prediction and Tracking

In this text, we present the source code implementation of the Kalman filter, an algorithm widely used for predictive tracking in video sequences. The Kalman filter operates through a series of mathematical equations that predict future states and update these predictions based on new measurements. The core implementation includes two main functions: predict() which projects the current state forward using the state transition matrix, and update() which incorporates new measurements using the Kalman gain calculation. This algorithm finds applications in diverse fields such as robotic navigation, object tracking, and audio signal processing. We believe this source code will help interested individuals better understand the algorithm's implementation and practical applications. The code structure demonstrates key components including state vectors, covariance matrices, and measurement models. Additionally, we recommend exploring related algorithms and techniques such as extended Kalman filters and particle filters to enhance your understanding and application of Kalman filtering in complex scenarios.