Tracking of Moving Objects Based on Extended Kalman Filter
The paper deals with the moving object tracking in dynamic environments, which is one of the most important problems in the field of computer vision. Over the last decade, an intensive work has been extensively done to create smart, autonomous vehicles that provide very precise and fast algorithms for the object detection and tracking. Our paper elaborates and demonstrates how it can be possible to monitor the trajectory of moving objects with high precision using sensor data, where the detection has been previously done. The standard Kalman Filter is described as an introduction to the Extended Kalman Filter (EKF) which was used for the algorithm implementation. Therefore, a problem of choosing model equations is also described, as well as the KITTI dataset used for the object detection. The main contribution of this paper includes an algorithm for the trajectory tracking that is capable to predict the position of moving objects. This algorithm is verified by experiments using realistic dataset.