Tracking multiple interacting objects represents a challenging area in computer vision. The tracking problem in general can be formulated as the task of recovering the spatio-temporal trajectories for an unknown number of objects appearing and disappearing at arbitrary times. Observations are noisy, their origin is unknown, generated by true detections or false alarms. Data association and the estimation of object states are two crucial tasks to be solved in this context. This work describes a novel, computationally efficient tracking approach to generate consistent trajectories. First, trajectory segments are created by analyzing the spatio-temporal data distribution using local principal component analysis. Subsequently, linking between trajectory segments is carried out relying on spatial proximity and kinematic smoothness constraints. Tracking results are demonstrated in the context of human tracking and compared to results of a frame-to-frame-based tracking approach.