Improving Surveillance Using Cooperative Target Observation

Authors

  • Rashi Aswani International Institute of Information Technology - Hyderabad
  • Sai Krishna Munnangi International Institute of Information Technology - Hyderabad
  • Praveen Paruchuri International Institute of Information Technology - Hyderabad

DOI:

https://doi.org/10.1609/aaai.v31i1.10707

Keywords:

Cooperative Target Observation, Surveillance Applications, K-means, Target Prediction, Memorization, BRLP-CTO, Adjustable Randomization

Abstract

The 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 including surveillance. In CTO problem, the observer agents attempt to maximize the collective time during which each moving target is being observed by at least one observer in the area of interest. However, most of the prior works for the CTO problem consider the targets 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 and provide experimental validation. 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 observers actions can help to make their target observation strategy less predictable. As the fifth variant, we therefore introduce Adjustable Randomization into the best performing observer strategy where the observer can adjust the expected loss in reward due to randomization depending on the situation.

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Published

2017-02-12

How to Cite

Aswani, R., Munnangi, S. K., & Paruchuri, P. (2017). Improving Surveillance Using Cooperative Target Observation. Proceedings of the AAAI Conference on Artificial Intelligence, 31(1). https://doi.org/10.1609/aaai.v31i1.10707

Issue

Section

AAAI Technical Track: Multiagent Systems