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Detecting carried objects is one of the requirements for developing systemsto reason about activities involving people and objects. We present an approachto detect carried objects from a single video frame with a novel method thatincorporates features from multiple scales. Initially, a foreground mask in avideo frame is segmented into multi-scale superpixels. Then the human-likeregions in the segmented area are identified by matching a set of extractedfeatures from superpixels against learned features in a codebook. A carriedobject probability map is generated using the complement of the matchingprobabilities of superpixels to human-like regions and background information.A group of superpixels with high carried object probability and strong edgesupport is then merged to obtain the shape of the carried object. We appliedour method to two challenging datasets, and results show that our method iscompetitive with or better than the state-of-the-art.

From Superpixel to Human Shape Modelling for Carried Object Detection