In this paper, we revisit trajectory-based action detection in a potent and non-uniform way. Improved trajectories have been proven to be an effective model for motion description in action recognition. In temporal action localization, however, this approach is not efficiently exploited. Trajectory features extracted from uniform video segments result in significant performance degradation due to two reasons: (a) during uniform segmentation, a significant amount of noise is often added to the main action and (b) partial actions can have negative impact in classifier's performance. Since uniform video segmentation seems to be insufficient for this task, we propose a two-step supervised non-uniform segmentation, performed in an online manner. Action proposals are generated using either 2D or 3D data, therefore action classification can be directly performed on them using the standard improved trajectories approach. We experimentally compare our method with other approaches and we show improved performance on a challenging online action detection dataset.