  We consider the problem of tracking multiple, unknown, and time-varying
numbers of objects using a distributed network of heterogeneous sensors. In an
effort to derive a formulation for practical settings, we consider limited and
unknown sensor field-of-views (FoVs), sensors with limited local computational
resources and communication channel capacity. The resulting distributed
multi-object tracking algorithm involves solving an NP-hard multidimensional
assignment problem either optimally for small-size problems or sub-optimally
for general practical problems. For general problems, we propose an efficient
distributed multi-object tracking algorithm that performs track-to-track fusion
using a clustering-based analysis of the state space transformed into a density
space to mitigate the complexity of the assignment problem. The proposed
algorithm can more efficiently group local track estimates for fusion than
existing approaches. To ensure we achieve globally consistent identities for
tracks across a network of nodes as objects move between FoVs, we develop a
graph-based algorithm to achieve label consensus and minimise track
segmentation. Numerical experiments with a synthetic and a real-world
trajectory dataset demonstrate that our proposed method is significantly more
computationally efficient than state-of-the-art solutions, achieving similar
tracking accuracy and bandwidth requirements but with improved label
consistency.
