Video phylogeny research about joint analysis of correlated video sequences has shown the possibility of developing interesting forensic applications. As an example, it is possible to study the provenance of near-duplicate (ND) video sequences, i.e., videos generated from the same original one through content preserving transformations. To perform this kind of analysis, accurate detection of ND videos is paramount. In this paper, we propose an algorithm for ND video detection and clustering in a challenging setup. Specifically, we analyze a scenario in which many videos, depicting the same event, are recorded by different users. This situation is critical as non-ND videos acquired from very close viewpoints run the risk of being incorrectly detected as ND. The proposed approach leverages on robust hashing properties and the concept of sensor noise traces.