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Automatic target detection in rangefinder data and spatio-temporal association using Bayesian Networks . Application to Vehicle Tracking

The perception and recognition of environmental facing the extraction problems of relevant information in a variety of available information. For this, it is necessary to define a collection system capable to provide this relevant information to follow successfully reliably state changes of objects in a scene. In addition, the estimation of dynamic characteristics of objects in a scene can be seen as a classic problem of multiple tracking objects. In this paper, we focus on the data type of distance and angle coming from a laser rangefinder. Two methods of detecting objects in the scene are presented and compared. One is based on a hierarchical classification of the measures and the other derived from the method of dynamic clusters. The object tracking is achieved by Kalman filtering. This filter is coupled with a spatio-temporal association method. This method maintains the tracks, their creation or destruction. A dynamic Bayesian network uses the redundancy of information on each of the objects as well as the characteristics of the sensor used to calculate the association probability of an observation to a track. Results are presented on simulated data (radar type) and actual (type laser rangefinder) in the context of tracking vehicles on the highway.