The KITTI Vision Benchmark Suite

When tracking multiple targets in crowded
scenarios, modeling mutual exclusion between
distinct targets becomes important at two
levels: (1) in data association, each
target observation should support at most one
trajectory and each trajectory should be
assigned at most one observation per frame; (2)
in trajectory estimation, two trajectories
should remain spatially separated at all times
to avoid collisions. Yet, existing trackers
often sidestep these important constraints. We
address this using a mixed discrete-continuous
conditional random field (CRF) that explicitly
models both types of constraints: Exclusion
between conflicting observations with
supermodular pairwise terms, and exclusion
between trajectories by generalizing
global label costs to suppress the co-
occurrence of incompatible labels
(trajectories). We develop an expansion
move-based MAP estimation scheme that handles
both non-submodular constraints and pairwise
global label costs. Furthermore, we perform a
statistical analysis of ground-truth
trajectories to derive appropriate CRF
potentials for modeling data fidelity, target
dynamics, and inter-target occlusion.

Using Regionlets Detections

outlierCost=995.0662
labelCost=2390.744
unaryFactor=46.6913
persistenceFactor=1.1155
curvatureFactor=0
slopeFactor=0.002317
proxcostFactor=755.4725
exclusionFactor=244.6583
pairwiseFactor=1.5128

@inproceedings{Milan2013CVPR,
Author = {Anton Milan and Konrad Schindler
and Stefan Roth},
Booktitle = {CVPR},
Title = {Detection- and Trajectory-Level
Exclusion in Multiple Object Tracking},
Year = {2013}
}