Learning Patterns of Activity Using Real-Time Tracking
IEEE Transactions on Pattern Analysis and Machine Intelligence
Introduction to Algorithms
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ICPR '96 Proceedings of the International Conference on Pattern Recognition (ICPR '96) Volume III-Volume 7276 - Volume 7276
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ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Tracking a moving object with a binary sensor network
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Poster abstract: cooperative tracking with binary-detection sensor networks
Proceedings of the 1st international conference on Embedded networked sensor systems
A line in the sand: a wireless sensor network for target detection, classification, and tracking
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IPSN '05 Proceedings of the 4th international symposium on Information processing in sensor networks
IPSN '05 Proceedings of the 4th international symposium on Information processing in sensor networks
Proceedings of the 4th international conference on Embedded networked sensor systems
Probabilistic detection of mobile targets in heterogeneous sensor networks
Proceedings of the 6th international conference on Information processing in sensor networks
Tracking multiple targets using binary proximity sensors
Proceedings of the 6th international conference on Information processing in sensor networks
IPSN'03 Proceedings of the 2nd international conference on Information processing in sensor networks
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IPSN'03 Proceedings of the 2nd international conference on Information processing in sensor networks
Counting targets with mobile sensors in an unknown environment
ALGOSENSORS'07 Proceedings of the 3rd international conference on Algorithmic aspects of wireless sensor networks
Minimalist counting in sensor networks (Noise helps)
Ad Hoc Networks
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We consider the problem of counting a set of discrete point targets using a network of sensors under a minimalistic model. Each sensor outputs a single integer, the number of distinct targets in its range, but targets are otherwise indistinguishable to sensors: no angles, distances, coordinates, or other target-identifying measurements are available. This minimalistic model serves to explore the fundamental performance limits of low-cost sensors for such surveillance tasks as estimating the number of people, vehicles or ships in a field of interest to first degree of approximation, to be followed by more expensive sensing and localization if needed. This simple abstract setting allows us to explore the intrinsic complexity of a fundamental problem, and derive rigorous worst-case performance bounds. We show that even in the 1-dimensional setting (for instance, sensors counting vehicles on a road), the problem is non-trivial: target count can be estimated within relative accuracy of factor $\sqrt{2}$ and this is the best possible in the worst-case. We then address additional questions related to constructing feasible target placements, and noisy counters. In two dimensions, the problem is considerably more complicated: a constant-factor approximation is impossible. Our algorithms and analysis can easily handle some of the non-idealities of real sensors, such as asymmetric ranges and non-exact target counts.