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We describe a method to detect instances of a walking person carrying an object seen from a stationary camera. We take a correspondence-free motion-based recognition approach, that exploits known shape and periodicity cues of the human silhouette shape. Specifically, we subdivide the binary silhouette into four horizontal segments, and analyze the temporal behavior of the bounding box width over each segment. We posit that the periodicity and amplitudes of these time series satisfy certain criteria for a natural walking person, and deviations therefrom are an indication that the person might be carrying an object. The method is tested on 41 360×240 color outdoor sequences of people walking and carrying objects at various poses and camera viewpoints. A correct detection rate of 85% and a false alarm rate of 12% are obtained.