In several medical applications such as liver or kidney biopsies, an anatomical region needs to be continuously tracked during the intervention. In this paper, we propose a novel approach for automatically tracking deformable target within 2D ultrasound images. Our approach uses only dense information combined with a physically-based model and has therefore the advantage of not using any fiducial marker nor a priori knowledge on the anatomical environment. The physical model is represented by a mass-spring damper system driven by different types of forces where the external forces are obtained by maximizing image similarity metric between a reference target and a deformed target across the time. This deformation is represented by a parametric warping model where the optimal parameters are estimated from the intensity variation. This warping function is well-suited to represent localized deformations in the ultrasound images because it directly links the forces applied on each mass with the motion of all the pixels in its vicinity. The internal forces constrain the deformation to physically plausible motions, and reduce the sensitivity to the speckle noise. The approach was validated on simulated and real data, both for rigid and free-form motions of soft tissues. The results are very promising since the deformable target could be tracked with a good accuracy for both types of motion. Our approach opens novel possibilities for computer-assisted interventions where deformable organs are involved and could be used as a new tool for interactive tracking of soft tissues in ultrasound images.