Tracking moving objects across non-overlapping cameras

In this paper we present an approach for tracking people across non overlapping cameras. The approach proposed is based a multi-dimensional feature vector and its covariance, defining an appearance model of every detected blob in the network of cameras. The model integrates relative position, color and texture descriptors of each detected object. Association of objects across non-overlapping cameras is performed by matching detected objects appearance with past observations. Availability of tracking within every camera can further improve the accuracy of such association by matching several targets appearance models with detected regions. For this purpose we present an automatic clustering technique allowing to build a multi-valued appearance model from a collection of covariance matrices. The proposed approach does not require geometric or colorimetric calibration of the cameras. We will illustrate the method for tracking people in relatively crowded scenes in a collection of indoors cameras taken in a mass transportation site. We will present success and challenges yet to be addressed by the proposed approach.