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» Conférences d’après mars 2011 : nouveau site


Séminaire Vision artificielle / Équipe Willow

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People Tracking with a Multi-Camera Setup
François Fleuret (EPFL, Suisse)

11 janvier 2006

In this talk, I will show that in a multi-camera context, we can effectively track and estimate the locations of an a priori unknown number of individuals with good accuracy, despite complex occlusions.
Our algorithm initially estimates for each isolated frame a conditional probability of occupancy for every location on the ground plane, given binary images produced by a simple background subtraction procedure. We show that a simple Bayesian formulation leads to a large system of equations whose variables are the conditional marginal probabilities of occupancy at each location. This system can be solved iteratively at a reasonable speed (10 frames per second with two cameras and a 25cm accuracy). Despite the absence of temporal consistency and the poor quality of the input data, this procedure by itself provides accurate detection of individuals on isolated frames.
The results can be improved by combining these estimates obtained on a few tens of isolated frames into a classical HMM, taking into account both the color consistency and a simple motion model.
We demonstrate the quality of our results on several sequences. The full algorithm performs reliably on these test sequences, with no false negative or false positive, and an error of less than 30cm for more than 90% of the predicted locations.
If there is time left after this main subject, I will briefly introduce a more prospective topic: learning the appearance of an object from a single example. Instead of using a large number of pictures of the object to recognize, we use a labeled reference database of pictures of other objects to learn high-level invariance. We propose to build hundreds of random binary splits of the training set, chosen to keep together the images of any given object, and to combine those splits with a Bayesian rule into a posterior probability of similarity.
Joint work with J. Berclaz, R. Lengagne and P. Fua.

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François Fleuret François Fleuret (EPFL, Suisse)
Ecole polytechnique fédérale de Lausanne
Computer Vision Laboratory