Lionel Martin

Pearson-based mixture model for color object tracking

To track objects in video sequences, many studies have been done to characterize the target with respect to its color distribution. Most often, the Gaussian mixture model (GMM) is used to represent the object color density. In this paper, we propose to extend the normality assumption to more general families of distributions issued from the Pearson’s system. Precisely, we propose a method called Pearson mixture model (PMM), used in conjunction with Gaussian copula, which is dynamically updated to adapt itself to the appearance change of the object during the sequence. This model is combined with Kalman filtering to predict the position of the object in the next frame. Experimental results on gray-level and color video sequences show tracking improvements compared to classical GMM. Especially, the PMM seems robust to illumination variations, pose and scale changes, and also to partial occlusions, but its computing time is higher than the computing time of GMM.

Video pupil tracking for iris based identification

Currently, iris identification systems are not easy to use since they need a strict cooperation of the user during the snapshot acquisition process. Several acquisitions are generally needed to obtain a workable image of the iris for recognition purpose. To make the system more flexible and open to large public applications, we propose to work on the entire sequence acquired by a camera during the enrolment. Hence the recognition step can be applied on a selected number of the “best workable images” of the iris within the sequence. In this context, the aim of the paper is to present a method for pupil tracking based on a dynamic Gaussian Mixture Model (GMM) together with Kalman prediction of the pupil position along the sequence. The method has been experimented on a real video sequence captured by a near Infra-Red (IR) sensitive camera and has shown its effectiveness in nearly real time computing.

Three dimensional fingertip tracking in stereovision

This paper presents a real time estimation method of the three dimensional trajectory of a fingertip. Pointing with the finger is indeed a natural gesture for Human Computer Interaction. Our approach is based on stereoscopic vision, with two standard webcams. The hand is segmented with skin color detection, and the fingertip is detected by the analysis of the curvature of finger boundary. The fingertip tracking is carried out by a three dimensional Kalman filter, in order to improve the detection with a local research, centered on the prediction of the 3-D position, and to filter the trajectory to reduce the estimation error.

An image sensor with global motion estimation for micro camera module

We describe in this paper the building of a vision sensor able to provide video capture and the associated global motion between two consecutive frames. Our objective is to propose embedded solutions for mobile applications. The global motion considered here is the one typically produced by handheld devices movement, which is required for our purpose of video stabilization. We extract this global motion from local motion measures at the periphery of the image acquisition area. Thanks to this peculiar and “task-oriented” configuration, the resulting system architecture can take advantage of CMOS focal plane processing capabilities without sacrificing the sensor fill factor. Our approach is currently implemented in a CMOS 0.13μm technology.