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Abstract. This paper demonstrates innovative techniques for estimating the trajectory of a soccer ball from multiple fixed cameras. Since the ball is nearly always moving and frequently occluded, its size and shape appearance varies over time and between cameras. Knowledge about the soccer domain is utilised and expressed in terms of field, object and motion models to distinguish the ball from

This paper tackles the problem of robust change detection in image sequences from static cameras. Motion cues are detected using frame differencing with an adaptive background estimation modelled by a mixture of Gaussians. Illumination invariance and elimination or detection of shadows is achieved by using a colour chromaticity representation of the image data. The combination of the colour-and intensity-based models results in some promising applications.

This paper presents a framework for multi-object tracking from a single fixed camera. The region-based representations of each object are tracked and predicted using a Kalman filter. A scene model is created to help predict and interpret the occluded or exiting objects. Unlike the traditional blind tracking during occlusion, the object states are updated using partial observations whenever available. The observability of each object depends on the predictive measurement of the object, the foreground region measurement, and perhaps the scene model. This makes the algorithm more robust in terms of both qualitative and quantitative criteria.