![](bg7.png)
(a) N
FA
(without non maxima suppres-
sion)
(b) λ
c
(without non maxima suppres-
sion)
(c) λ
c
(with non maxima suppression)
Figure 1: Clutter parameter analysis on S2L1: (a) with detector confidence scores, (b), (c) without detector confidence scores.
tracking. In this paper, we have presented an adapted
PHD recursion that incorporates detector confidence
scores to mimic state-dependent false alarms as well
as a practical SMC implementation that can be in-
tegrated into the min-cost flow network formulation
of Wojke and Paulus (2016). Our experiments re-
vealed that integration of detector confidence scores
has considerable impact on overall applicability of
the PHD filter and, in general, our approach achieves
results competitive with the current state of the art.
FISST and the PHD filter may help to solve open
multi-object tracking problems and there is ample op-
portunity for future work, e.g., integration of appear-
ance information, application of more complex global
data association formulations, and object group track-
ing.
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