Overall Objectives
Scientific Foundations
Application Domains
New Results
Contracts and Grants with Industry

Section: Contracts and Grants with Industry

Tracking in a binary sensor network — contract with DGA / CEP (centre d'expertise parisien)

Participants : Jean–Pierre Le Cadre, Adrien Ickowicz, Nicolas Ramin.

See  4.1 and  6.11 .

INRIA contract ALLOC 2338 — April 2007 to October 2009.

This contract deals with surveillance of large zones via a network of video sensors. Of course, sensor outputs can be treated in a centralized architecture. However, centralized architectures suffer from serious drawbacks. Communication constraints (e.g. bandwidth) are frequently evoked, but still more fundamentally we have to face many problems inherent of this architecture, like:

Overall, there is a strong need for extracting a global picture at the network level. This means that we have to focus on the level of information we can extract at the sensor level and how to fuse them. Work has been done on the first point, using both simulated and real video sequences. The less informative level of information is the binary one. However, there is a fundamental difference between a {0, 1} information and {-, + } information. A general {0, 1} information corresponds to a detection / non–detection information. Such architecture has been widely studied in a distributed detection framework, but is not well suited to our context. However {0, 1} information is especially interesting if the detection process includes geographic constraints like proximity, field–of–view, etc. The {-, + } information corresponds to a motion information: the object gets closer or is going far away. At the network level, this is a very rich information which can present definite advantages (robustness, multi–target tracking). However, its interest depends on the network density. So, it is also necessary to consider various and complementary decentralized architectures according to the sensing capabilities, the target behaviors and, overall, the combinatorial complexity of the problem. Work has been done for defining processings and architectures adapted to this context.

Work has been devoted to the processing of image sequences for extracting the binary information. After considering the divergence of a local motion model, its estimation and use on real data, we turned toward a temporal analysis of the bearing information. More precisely, bearing rates and bearing rate changes give us an estimate of the (local) target behavior. It is thus possible to derive a local estimate of the ratio Im4 ${\mover r\#729 /r}$ , through purely passive measurements, at the sensor level. Although this analysis is purely local, it performs satisfactorily on simulated sequences.

Concerning the information processing at the sensor level, work has been done in three directions. First, the {-, + } informations can be summarized via the times of CPA (closest point approach) on the various sensors. A complete analysis of the target motion analysis has been done in this framework, with various models of target motion. Then, we turned toward the analysis of the spatio–temporal {-, + } informations. An original method based on a separation principle allows us to obtain an estimate of the target motion parameters via multiple SVM. Second, we considered random target trajectories and target tracking. To that aim, a specific method based on multiple corrections has been developed.


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