In this paper, re-identification techniques are exploited to add context awareness to a multi-target tracker and enhance its tracking performance, in an online manner. To achieve that, targets are labeled as independent, occluders or occluded ones, based on the completeness of their appearance information. For each category, a different tracking strategy is employed to achieve the optimal results. In cases of tracking failure, an online automated re-identification technique is proposed, to alleviate multiple identity assignments to the same target. Experimental evaluation conducted on the CAVIAR and PETS 2009 datasets shows that the proposed mechanism enhances tracking performance compared to a baseline tracker and achieves competitive performance with state of the art methods.