  Multiple-Object Tracking (MOT) is of crucial importance for applications such
as retail video analytics and video surveillance. Object detectors are often
the computational bottleneck of modern MOT systems, limiting their use for
real-time applications. In this paper, we address this issue by leveraging on
an ensemble of detectors, each running every f frames. We measured the
performance of our system in the MOT16 benchmark. The proposed model surpassed
other online entries of the MOT16 challenge in speed, while maintaining an
acceptable accuracy.
