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Camera networks are being deployed for various applications like security and surveillance, disaster response and environmental modeling. However, there is little automated processing of the data. Moreover, most methods for multicamera analysis are centralized schemes that require the data to be present at a central server. In many applications, this is prohibitively expensive, both technically and economically. In this paper, we investigate distributed scene analysis algorithms by leveraging upon concepts of consensus that have been studied in the context of multiagent systems, but have had little applications in video analysis. Each camera estimates certain parameters based upon its own sensed data which is then shared locally with the neighboring cameras in an iterative fashion, and a final estimate is arrived at in the network using consensus algorithms. We specifically focus on two basic problems-tracking and activity recognition. For multitarget tracking in a distributed camera network, we show how the Kalman-Consensus algorithm can be adapted to take into account the directional nature of video sensors and the network topology. For the activity recognition problem, we derive a probabilistic consensus scheme that combines the similarity scores of neighboring cameras to come up with a probability for each action at the network level. Thorough experimental results are shown on real data along with a quantitative analysis.

Distributed algorithms have recently gained immense popularity. With regards to computer vision applications, distributed multi-target tracking in a camera network is a fundamental problem. The goal is for all cameras to have accurate state estimates for all targets. Distributed estimation algorithms work by exchanging information between sensors that are communication neighbors. Vision-based distributed multi-target state estimation has at least two characteristics that distinguishes it from other applications. First, cameras are directional sensors and often neighboring sensors may not be sensing the same targets, i.e., they are naive with respect to that target. Second, in the presence of clutter and multiple targets, each camera must solve a data association problem. This paper presents an information-weighted, consensus-based, distributed multi-target tracking algorithm referred to as the Multi-target Information Consensus (MTIC) algorithm that is designed to address both the naivety and the data association problems. It converges to the centralized minimum mean square error estimate. The proposed MTIC algorithm and its extensions to non-linear camera models, termed as the Extended MTIC (EMTIC), are robust to false measurements and limited resources like power, bandwidth and the real-time operational requirements. Simulation and experimental analysis are provided to support the theoretical results.

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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. Multitarget 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. In this paper, we propose consensus-based distributed multi-target tracking algorithms in a camera network that are designed to address this issue of naivety. The estimation errors in tracking and data association, as well as the effect of naivety, are jointly addressed leading to the development of an informationweighted consensus algorithm, which we term as the Multitarget Information Consensus (MTIC) algorithm. The incorporation of the probabilistic data association mechanism makes the MTIC algorithm very robust to false measurements/clutter. Experimental analysis is provided to support the theoretical results.

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