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This paper describes a vehicle tracking method that uses texture, color, size, distance and trajectory as modeling features. Before the tracking task starts, a representation to detect the target vehicles is constructed. Two methods are used to perform vehicle detection. The first method uses color, texture and a background model to detect the vehicle regions. The second one uses texture and lightness differences between the current frame and a previously modeled background. An experimental comparison of the two vehicle detection methods is performed both qualitatively and quantitatively in order to choose the most suitable one. Vehicle tracking is then achieved through a multiple hypotheses tracking method that integrates size, color, distance and trajectory in a single similarity vector by using a hierarchical analysis.

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We present preliminary results of an algorithm for detecting obstacle-free regions in indoor environments using both color and texture information for visual robot navigation. By modeling color information in the L*u*v* color space, a color-based segmentation is performed to find similar regions. This segmentation yields a set of regions that are joined together into single areas using texture information. Texture-based similarity measures between segmented regions are computed using ANOVA (Analysis of Variance), which produces the final obstacle-free regions. These regions are then used as visual information to drive a robot towards the scene horizon, i.e. the boundary between visible free space and unexplored areas. The performance of our algorithm was tested on several sequences of images taken from office corridors.