  This paper assumes prior detections of multiple targets at each time instant,
and uses a graph-based approach to connect those detections across time, based
on their position and appearance estimates. In contrast to most earlier works
in the field, our framework has been designed to exploit the appearance
features, even when they are only sporadically available, or affected by a
non-stationary noise, along the sequence of detections. This is done by
implementing an iterative hypothesis testing strategy to progressively
aggregate the detections into short trajectories, named tracklets.
Specifically, each iteration considers a node, named key-node, and investigates
how to link this key-node with other nodes in its neighborhood, under the
assumption that the target appearance is defined by the key-node appearance
estimate. This is done through shortest path computation in a temporal
neighborhood of the key-node. The approach is conservative in that it only
aggregates the shortest paths that are sufficiently better compared to
alternative paths. It is also multi-scale in that the size of the investigated
neighborhood is increased proportionally to the number of detections already
aggregated into the key-node. The multi-scale nature of the process and the
progressive relaxation of its conservativeness makes it both computationally
efficient and effective.
  Experimental validations are performed extensively on a toy example, a 15
minutes long multi-view basketball dataset, and other monocular pedestrian
datasets.
