Several algorithms for object tracking, are developed, but our method is slightly different, it’s about how to adapt and implement such algorithms on mobile platform. We started our work by studying and analyzing feature matching algorithms, to highlight the most appropriate implementation technique for our case. In this paper, we propose a technique of implementation of the algorithm SURF (Speeded Up Robust Features), for purposes of recognition and object tracking in real time. This is achieved by the realization of an application on a mobile platform such a Raspberry pi, when we can select an image containing the object to be tracked, in the scene captured by the live camera pi. Our algorithm calculates the SURF descriptor for the two images to detect the similarity therebetween, and then matching between similar objects. In the second level, we extend our algorithm to achieve a tracking in real time, all that must respect raspberry pi performances. So, the first thing is setting up all libraries that the raspberry pi need, then adapt the algorithm with card’s performances. This paper presents experimental results on a set of evaluation images as well as images obtained in real time.