  We present a novel formulation of the multiple object tracking problem which
integrates low and mid-level features. In particular, we formulate the tracking
problem as a quadratic program coupling detections and dense point
trajectories. Due to the computational complexity of the initial QP, we propose
an approximation by two auxiliary problems, a temporal and spatial association,
where the temporal subproblem can be efficiently solved by a linear program and
the spatial association by a clustering algorithm. The objective function of
the QP is used in order to find the optimal number of clusters, where each
cluster ideally represents one person. Evaluation is provided for multiple
scenarios, showing the superiority of our method with respect to classic
tracking-by-detection methods and also other methods that greedily integrate
low-level features.
