Lazy inference on object identities in wireless sensor networks

Tracking the identities of moving objects is an important aspect of most multi-object tracking applications. Uncertainty in sensor data, coupled with the intrinsic difficulty of the data association problem, suggests probabilistic formulations over the set of possible identities. While an explicit representation of a distribution over all associations may require exponential storage and computation, in practice the information provided by this distribution is accessed only in certain stylized ways, as when asking for the identity of a given track, or the track with a given identity. Exploiting this observation, we proposed in [1] a practical solution to this problem based on maintaining marginal probabilities and demonstrated its effectiveness in the context of tracking within a wireless sensor network. That method, unfortunately, requires extensive communication in the network whenever new identity observations are made, in order for normalization operations to keep the marginals co...

• Type: Conference
• Year: 2005
• Where: IPSN
• Authors: Jaewon Shin, Nelson Lee, Sebastian Thrun, Leonidas J. Guibas
• Keywords: Certain Stylized Ways, Data Association Problem, IPSN 2005, Multi-object Tracking Applications, Sensor Networks