Digital Particle Image Velocimetry (DPIV) aims at flow visualisation and measurement of flow dynamics. The fluid is seeded with particles that follow the flow and efficiently scatter light. Traditionally, FFT based correlation techniques have been used to estimate the displacements of the particles in a digital PIV sequence. Recently, an optical flow estimation technique developed in computer vision has been successfully applied to DPIV. In this paper we study the DPIV-efficiency of another group of tracking approaches, the feature tracking techniques. It is concluded that feature tracking algorithms applied to DPIV are a good alternative to both the correlation and the optical flow algorithms.