Survey of Challenges in Labeled Random Finite Set Distributed Multi-Sensor Multi-Object Tracking

Abstract

In recent years, increasing interest in distributed sensing networks has led to a demand for robust multi-sensor multi-object tracking (MOT) methods that can take advantage of large quantities of gathered data. However, distributed sensing has unique challenges stemming from limited computational resources, limited bandwidth, and complex network topology that must be considered within a given tracking method. Several recently developed methods that are based upon the random finite set (RFS) have shown promise as statistically rigorous approaches to the distributed MOT problem. Among the most desirable qualities of RFS-based approaches is that they are derived from a common mathematical framework, finite set statistics, which provides a basis for principled fusion of full multi-object probability distributions. Yet, distributed labeled RFS tracking is a still-maturing field of research, and many practical considerations must be addressed before large-scale, real-time systems can be implemented. For example, methods that use label-based fusion require perfect label consistency of objects across sensors, which is impossible to guarantee in scalable distributed systems. This paper provides a survey of the challenges inherent in distributed tracking using labeled RFS methods. An overview of labeled RFS filtering is presented, the distributed MOT problem is characterized, and recent approaches to distributed labeled RFS filtering are examined. The problems that currently prevent implementation of distributed labeled RFS trackers in scalable real-time systems are identified and demonstrated within the scope of several exemplar scenarios.