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Visual Sensor Networks (VSNs) exploit the processing and communication capabilities of modern smart cameras to handle a variety of applications such as security and surveillance, industrial monitoring, and critical infrastructure protection. The performance of VSNs can be severely degraded because of errors in the detection module. As a result, the performance of the higher-level application such as activity recognition, tracking, etc., also suffers due to the fact that in most cases the decision making process in VSNs assumes ideal detection capabilities for the cameras. Realizing that it is necessary to introduce robustness in the decision process this paper presents results towards uncertainty-aware VSNs. Specifically, we introduce a flexible uncertainty model that can be used to study the behaviour of missed detections in a camera network. We also show how to utilize the model to develop uncertainty-aware coordination and decision making solutions to improve the efficiency of VSNs. Our experimental results in an active vision application indicate that the proposed solution is able to improve the robustness and reliability of VSNs.

A camera uncertainty model for collaborative visual sensor network applications