A temporal fusion strategy for cross-camera data association
The purpose of this work is the design of a distributed vision system for vehicles tracking over wide areas. The tracking is performed by the re-identification of objects perceived by distant non-overlapping cameras. The data association is controlled by a temporal reasoning scheme. Decisions combine temporal and visual information. The visual information is composed by the 3D dimension and the normalised colour histogram of detected objects. Temporal constraints based on an acceleration model between nodes, are continuously updated with respect to the observed traffic behaviour. These constraints maintain a dynamic lifespan for all tracked objects. The management of the uncertainty represents an important component of the system. Statistical measurements are exploited at the sensor level information and a possibilistic approach permits to manage the ambiguities of the data association stage.
temporal reasoning distributed sensing multi-camera tracking uncertainty management