On an Optimization Problem in Sensor Selection
Discrete Event Dynamic Systems
Sensor node selection for execution of continuous probabilistic queries in wireless sensor networks
Proceedings of the ACM 2nd international workshop on Video surveillance & sensor networks
A formal analysis of why heuristic functions work
Artificial Intelligence
Timeline-based information assimilation in multimedia surveillance and monitoring systems
Proceedings of the third ACM international workshop on Video surveillance & sensor networks
The sensor selection problem for bounded uncertainty sensing models
IPSN '05 Proceedings of the 4th international symposium on Information processing in sensor networks
Context-Based Multimedia Sensor Selection Method
AVSS '09 Proceedings of the 2009 Sixth IEEE International Conference on Advanced Video and Signal Based Surveillance
FQAS'11 Proceedings of the 9th international conference on Flexible Query Answering Systems
A RELIEF-based modality weighting approach for multimodal information retrieval
Proceedings of the 2nd ACM International Conference on Multimedia Retrieval
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A multimedia analysis system utilizes a set of correlated media streams, each of which, we assume, has a confidence level and a cost associated with it, and each of which partially helps in achieving the system goal. However, the fact that at any instant, not all of the media streams contribute towards a system goal brings up the issue of finding the best subset from the available set of media streams. For example, a subset of two video cameras and two microphones could be better than any other subset of sensors at some time instance to achieve a surveillance goal (e.g. event detection). This article presents a novel framework that finds the optimal subset of media streams so as to achieve the system goal under specified constraints. The proposed framework uses a dynamic programming approach to find the optimal subset of media streams based on three different criteria: first, by maximizing the probability of achieving the goal under the specified cost and confidence; second, by maximizing the confidence in the achieved goal under the specified cost and probability with which the goal is achieved; and third, by minimizing the cost to achieve the goal with a specified probability and confidence. Each of these problems is proven to be NP-Complete. From an AI point of view, the solution we propose is heuristic-based, and for each criterion, utilizes a heuristic function which for a given problem, combines optimal solutions of small-sized subproblems to yield a potential near-optimal solution to the original problem. The proposed framework allows for a tradeoff among the aforementioned three criteria, and offers the flexibility to compare whether any one set of media streams of low cost would be better than any other set of higher cost, or whether any one set of media streams of high confidence would be better than any other set of low confidence. To show the utility of our framework, we provide the experimental results for event detection in a surveillance scenario.