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