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.

翻译：我们考虑利用分布式异构传感器网络对未知且时变数量的多个目标进行跟踪的问题。为推导适用于实际场景的公式，我们考虑了传感器有限且未知的视场、本地计算资源受限以及信道容量有限的传感器。由此产生的分布式多目标跟踪算法涉及求解一个NP难的多维分配问题，对于小规模问题采用最优求解，对于一般实际问题则采用次优求解。针对一般问题，我们提出了一种高效的分布式多目标跟踪算法，该算法通过基于聚类的状态空间分析（将状态空间转换为密度空间）来执行航迹-航迹融合，从而降低分配问题的复杂度。与现有方法相比，所提算法能更高效地对局部航迹估计进行分组以实现融合。为确保目标在视场间移动时，网络中所有节点上的航迹具有全局一致的标识，我们开发了一种基于图论的算法以实现标签共识并最小化航迹分段。基于合成轨迹数据集和真实轨迹数据集的数值实验表明，与最先进方案相比，我们的方法计算效率显著提升，在达到相似跟踪精度和带宽需求的同时，实现了更优的标签一致性。