Information Weighted Consensus for Distributed Estimation in Vision Networks.

摘要

Due to their high fault-tolerance, ease of installation and scalability to large networks, distributed algorithms have recently gained immense popularity in the sensor networks community, especially in computer vision. Multi-target tracking in a camera network is one of the fundamental problems in this domain. Distributed estimation algorithms work by exchanging information between sensors that are communication neighbors. Since most cameras are directional sensors, it is often the case that neighboring sensors may not be sensing the same target. Such sensors that do not have information about a target are termed as ''naive'' with respect to that target. State-of-the-art distributed state estimation algorithms (e.g., the Kalman Consensus Filter (KCF)) in the sensor networks community are not directly applicable to tracking applications in camera networks mainly due to this naivety issue. In our work, we propose generalized distributed algorithms for state estimation in a sensor network taking the naivety issue into account.;For multi-target tracking, along with the tracking framework, a data association step is necessary where the measurements in each camera's view are associated with the appropriate targets' tracks. At first, under the assumption that the data association is given, we develop distributed state estimation algorithms addressing the naivety issue. In this process, first, we propose the Generalized Kalman Consensus Filter (GKCF) where an information-weighting scheme is utilized to account for the naivety issue. Next, we propose the Information-weighted Consensus Filter (ICF) which can achieve optimal centralized performance while also accounting for naivety. This is the core contribution of this thesis. Next, we introduce the aspect of multi-target tracking where a probabilistic data association scheme is incorporated in the distributed tracking scheme resulting the Multi-Target Information Consensus (MTIC) algorithm. The incorporation of the probabilistic data association mechanism makes the MTIC algorithm very robust to false measurements/clutter.;The aforementioned algorithms are derived under the assumption that the measurements are related to the state variables using a linear relationship. However, in general, this is not true for many sensors including camera sensors. Thus, to account for the non-linearity in the observation model, we propose non-linear extensions of the previous algorithms which we denote as the Extended ICF (EICF) and the Extended MTIC (EMTIC) algorithms. In-depth theoretical and experimental analysis are provided to compare these algorithms with existing ones.