A Novel Tracking Framework Using Kalman Filtering and Elastic Matching

Abstract

Abstract. A novel region-based multiple object tracking framework based on Kalman filtering and elastic matching is proposed. The proposed Kalman filtering-elastic matching model is general in two significant ways. First, it is suitable for tracking of both, rigid and elastic objects. Second, it is suitable for tracking using both, fixed cameras and moving cameras since the method does not rely on background subtraction. The elastic matching algorithm exploits both the spectral features and structural features of the tracked objects, making it more robust and general in the context of object tracking. The proposed tracking framework can be viewed as a generalized Kalman filter where the elastic matching algorithm is used to measure the velocity field which is then approximated using B-spline surfaces. The control points of the B-spline surfaces are directly used as the tracking variables in a grid-based Kalman filtering model. The limitations of the Gaussian distribution assumption in the Kalman filter are overcome by the large capture range of the elastic matching algorithm. The B-spline approximation of the velocity field is used to update the spectral features of the tracked objects in the grid-based Kalman filter model. The dynamic nature of these spectral features are subsequently used to reason about occlusion. Experimental results on tracking of multiple objects in real-time video are presented.