Tracking of Multiple Objects Using Optical Flow Based Multiscale Elastic Matching

A novel hybrid region-based and contour-based multiple object tracking model using optical flow based elastic matching is proposed. The proposed elastic matching model is general in two significant ways. First, it is suitable for tracking of both, rigid and deformable objects. Second, it is suitable for tracking using both, fixed cameras and moving cameras since the model does not rely on background subtraction. The elastic matching algorithm exploits both, the spectral features and contour-based features of the tracked objects, making it more robust and general in the context of object tracking. The proposed elastic matching algorithm uses a multiscale optical flow technique to compute the velocity field. This prevents the multiscale elastic matching algorithm from being trapped in a local optimum unlike conventional elastic matching algorithms that use a heuristic search procedure in the matching process. The proposed elastic matching based tracking framework is combined with Kalman filter in our current experiments. The multiscale elastic matching algorithm is used to compute the velocity field which is then approximated using B-spline surfaces. The control points of the B-spline surfaces are used directly as the tracking variables in a Kalman filtering model. The B-spline approximation of the velocity field is used to update the spectral features of the tracked objects in the 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.