一种基于Kalman滤波的视频对象跟踪方法

为了更加准确地预测对象的位置和运动，利用刚体运动模型导出最佳Kalman系数，通过Kalman反馈滤波器对Moscheni等人提出的视频对象分割与跟踪算法进行改进，提出了一种将离散Kalman滤波技术用于视频序列的对象跟踪方法。这种方法可用于有关场景描述的各种应用领域中，如在机器视觉的研究中，对动态场景进行分析与理解；在基于对象的视频编码中（如MPEG-4），对视频对象进行分割后，分别进行编码，从而改善编码的可分级性及编码效率。实验结果表明，采用这种方法可以有效地改善时间-空间分割和目标跟踪，有助于更好地理解动态场景，并表现出良好的鲁棒性。

Kalman Filter for Video Object Segmentation and Tracking

In this paper, a technique based on a discrete Kalman filter algorithm is proposed to follow the trajectory of the objects. The aim is to obtain a precise prediction of their position and motion. The accurate prediction improves both the recursive spatio-temporal segmentation and object tracking performances, enabling a high level understanding of the scene dynamics. The derived scene representation obtained finds applications in various domains. For instance, it is very well suited for dynamic scene analysis where a deep scene understanding is required. Typical examples are scene understanding and robot vision. It is also very appealing in the context-based video coding(MPEG-4). Experimental results have shown that this method is able to integrate over time the temporal information for each object and to interpolate or extrapolate its trajectory, correctly predict the position and the motion of temporal coherent objects. However, if the object has performed maneuvers, the Kalman filter fails in its prediction. In order to decide when is convenient to use the last estimated motion of the object instead of the Kalman prediction a test based on motion compensation error is used. Finally the proposed algorithm has shown its robustness in the presence of object occlusions.