4 research outputs found
Tracking many objects with many sensors
No full textKeeping track of multiple objects over time is a problem that arises in many real-world domains. The problem is often complicated by noisy sensors and unpredictable dynamics. Previous work by Huang and Russell, drawing on the data association literature, provided a probabilistic analysis and a threshold-based approximation algorithm for the case of multiple objects detected by two spatially separated sensors. This paper analyses the case in which large numbers of sensors are involved. We show that the approach taken by Huang and Russell, who used pairwise sensor-based appearance probabilities as the elementary probabilistic model, does not scale. When more than two observations are made, the objects ' intrinsic properties must be estimated. These provide the necessary conditional independencies to allow a spatial decomposition of the global probability model. We also replace Huang and Russell's threshold algorithm for object identification with a polynomial-time approximation scheme based on Markov chain Monte Carlo simulation. Using sensor data from a freeway traffic simulation, we show that this allows accurate estimation of long-range origin/destination information even when the individual links in the sensor chain are highly unreliable.
Penalized Linear Discriminant Analysis of in Situ Hyperspectral Data for Conifer Species Recognition
No full textUsing in situ hyperspectral measurements collected in the Sierra Nevada Mountains in California, the authors discriminate six species of conifer trees using a recent, nonparametric statistics technique known as penalized discriminant analysis (PDA). A classification accuracy of 76% is obtained. Their emphasis is on providing an intuitive, geometric description of PDA that makes the advantages of penalization clear. PDA is a penalized version of Fisher\u27s linear discriminant analysis (LDA) and can greatly improve upon LDA when there are a large number of highly correlated variables
