Kalman Filter-Based Object Tracking Algorithm

In the fields of computer vision and artificial intelligence, the Kalman filter-based object tracking algorithm is a widely adopted technique. The core principle involves using a Kalman filter to estimate and predict the target's position and velocity, enabling robust object tracking. The algorithm typically operates in two main steps: prediction (forecasting the target's next state) and update (correcting the state based on new measurements). Compared to other tracking methods, this approach offers superior accuracy and computational efficiency, making it suitable for real-time applications such as video surveillance, autonomous driving, and robotics. With the advancement of deep learning, Kalman filter-based tracking has been further optimized through hybrid approaches—for instance, combining CNN-based detection with Kalman filter motion modeling—enhancing its adaptability. Future developments are expected to expand its applicability across broader domains.