IIIT Hyderabad Publications |
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Improving Surveillance using Cooperative Target ObservationAuthor: Rashi Aswani Date: 2018-03-29 Report no: IIIT/TH/2018/14 Advisor:Praveen Paruchuri AbstractThe Cooperative Target Observation (CTO) problem has been of great interest in the multi-agents and robotics literature due to the problem being at the core of a number of applications for example surveillance, inspection and search-and-rescue. In CTO problem, the observer agents attempt to maximize the collective time during which each moving target agent is being observed by at least one observer agent in the area of interest. The area of interest can include airport, border patrolling or public events. Micro-drones are typically used for such operations as with large gatherings and protests, or even when monitoring individuals, it is often difficult to maintain an overview. They can be pretty useful for surveillance allowing for a (top) view of the happenings on ground. The CTO problem in most of the prior works is solved by finding the best suited strategy for observer agents, considering the target agent’s movement to be Randomized. Given our focus on surveillance domain, we modify this assumption to make the targets strategic and present two target strategies namely Straight-line strategy and Controlled Randomization strategy. We then modify the observer strategy proposed in the literature based on the K-means algorithm to introduce five variants over the K-means algorithm and provide experimental validation across the strategies. In surveillance domain, it is often reasonable to assume that the observers may themselves be a subject of observation for a variety of purposes by unknown adversaries whose model may not be known. Randomizing the observer’s action can help to make their target observation strategy less predictable, so that the unknown adversary cannot harm the observer agent. As the fifth variant, we therefore introduce Adjustable Randomization into the best performing observer strategy among the four variants over K-means algorithm, where the observer can adjust the expected loss in reward due to randomization depending on the situation. Full thesis: pdf Centre for Data Engineering |
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