  We propose a 3D multi-object tracking (MOT) solution using only 2D detections
from monocular cameras, which automatically initiates/terminates tracks as well
as resolves track appearance-reappearance and occlusions. Moreover, this
approach does not require detector retraining when cameras are reconfigured but
only the camera matrices of reconfigured cameras need to be updated. Our
approach is based on a Bayesian multi-object formulation that integrates track
initiation/termination, re-identification, occlusion handling, and data
association into a single Bayes filtering recursion. However, the exact filter
that utilizes all these functionalities is numerically intractable due to the
exponentially growing number of terms in the (multi-object) filtering density,
while existing approximations trade-off some of these functionalities for
speed. To this end, we develop a more efficient approximation suitable for
online MOT by incorporating object features and kinematics into the measurement
model, which improves data association and subsequently reduces the number of
terms. Specifically, we exploit the 2D detections and extracted features from
multiple cameras to provide a better approximation of the multi-object
filtering density to realize the track initiation/termination and
re-identification functionalities. Further, incorporating a tractable geometric
occlusion model based on 2D projections of 3D objects on the camera planes
realizes the occlusion handling functionality of the filter. Evaluation of the
proposed solution on challenging datasets demonstrates significant improvements
and robustness when camera configurations change on-the-fly, compared to
existing multi-view MOT solutions. The source code is publicly available at
https://github.com/linh-gist/mv-glmb-ab.
