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Tracking the small object through clutter with adaptive particle filter

Cluttered background and occlusion cause large ambiguity in the tracking of video objects. When the object is small (like a soccer ball in broadcast game video signals), the ambiguity gets even more severe. In this paper, we propose an adaptive particle filter with effective proposal distribution to handle these situations. In the proposed tracking approach, motion estimation is embedded into the state transition to tackle abrupt motion changes and generate good proposal distributions. We also propose a mixture model to account for multiple hypotheses in the template correlation surface when estimating the appearance likelihood. In addition, motion continuity and trajectory smoothness are combined with template correlation in the observation likelihood to further filter out visual distracters. As an example of small object tracking, promising results of the ball tracking (as small as 30 pixels) in soccer game videos are presented to illustrate that the proposed scheme handles the cluttered background and occlusion effectively.