Abstract
In this paper, a novel consensus-based adaptive algorithm for distributed target tracking in large scale camera networks is presented, aimed at situations characterized by limited sensing range, high-level clutter, and possibly occulted targets. The concept of Integrated Probabilistic Data Association (IPDA) is introduced in the distributed adaptive tracker design so that the proposed algorithm, named IPDA Adaptive Consensus Filter (IPDA-ACF), incorporates probabilities of acquiring target-originated measurements, conditioned on either target perceivability or target existence. A distributed adaptation scheme represents the core element of the algorithm, allowing fast convergence under a large variety of operating conditions, emphasizing the influence of the nodes with the highest probability of obtaining target-originated measurements. A theoretical analysis of stability and reduction of noise influence allows getting an insight into the relationship between the local trackers and the global consensus scheme. A comparison with analogous existing methods done by extensive simulations shows that the proposed method achieves the best performance, in spite of lower communication and computation requirements.
Highlights
Recent rapid improvement in quality and resolution of imaging sensors and availability of low-cost smart cameras, together with the development of sensor network technology, have paved the way for creation of large scale camera networks
The algorithms deal with two types of the so-called “β -parameters, representing probabilities of getting a target-originated measurement conditioned either on target perceivability or on target existence; in this sense, we have the algorithms denoted as Integrated Probabilistic Data Association (IPDA)-ACF1 and IPDA-ACF2, respectively
The concept of target perceivability is a part of the general methodology of Integrated Probabilistic Data Association (IPDA) and represents a refinement of the concept of track existence and of target observability [13–15]
Summary
Recent rapid improvement in quality and resolution of imaging sensors and availability of low-cost smart cameras, together with the development of sensor network technology, have paved the way for creation of large scale camera networks. We assume cluttered environment and possibly temporarily occulted targets and propose a new distributed adaptive tracking algorithm for camera networks, representing an extension of ACF from [2] based on Integrated Probabilistic Data Association (IPDA); this algorithm will be denoted further as IPDA-ACF. The elegant Probabilistic Data Association (PDA) methodology has been successfully applied in this case, including a variety of very successful applications to radars, sonars, and electro-optic systems, e.g., [9–11, 20–25] This methodology is based on the implicit strong assumption that the target is always perceivable. The concept of target perceivability is a part of the general methodology of Integrated Probabilistic Data Association (IPDA) and represents a refinement of the concept of track existence (which does not address the possibility that a target cannot be detected) and of target observability (which presumes the presence of a target) [13–15]. It will be seen below that the choice between βi[,1j ](t) and βi[,2j ](t) offers an additional possibility for adaptation to the current situation concerning the target, the entire sensor network, and its environment
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