  This paper presents a novel hierarchical approach for the simultaneous
tracking of multiple targets in a video. We use a network flow approach to link
detections in low-level and tracklets in high-level. At each step of the
hierarchy, the confidence of candidates is measured by using a new scoring
system, ConfRank, that considers the quality and the quantity of its
neighborhood. The output of the first stage is a collection of safe tracklets
and unlinked high-confidence detections. For each individual detection, we
determine if it belongs to an existing or is a new tracklet. We show the effect
of our framework to recover missed detections and reduce switch identity. The
proposed tracker is referred to as TVOD for multi-target tracking using the
visual tracker and generic object detector. We achieve competitive results with
lower identity switches on several datasets comparing to state-of-the-art.
