In the near future, preventing collisions with fixed or moving, alive, and inanimate obstacles will appear to be a severe challenge due to the increased use of Unmanned Ground Vehicles (UGVs). Light Detection and Ranging (LIDAR) sensors and cameras are usually used in UGV to detect obstacles. The definite tracing and classification of moving obstacles is a significant dimension in developed driver assistance systems. It is believed that the perceived model of the situation can be improved by incorporating the obstacle classification. The present study indicated a multi-hypotheses monitoring and classifying approach, which allows solving ambiguities rising with the last methods of associating and classifying targets and tracks in a highly volatile vehicular situation. This method was tested through real data from various driving scenarios and focusing on two obstacles of interest vehicle, pedestrian.