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We propose a novel framework for searching for people in surveillance environments. Rather than relying on face recognition technology, which is known to be sensitive to typical surveillance conditions such as lighting changes, face pose variation, and low-resolution imagery, we approach the problem in a different way: we search for people based on a(More)
We consider the problem of parsing human poses and recognizing their actions in static images with part-based models. Most previous work in part-based models only considers rigid parts (e.g., torso, head, half limbs) guided by human anatomy. We argue that this representation of parts is not necessarily appropriate. In this paper, we introduce hierarchical(More)
We address the problem of human parsing using part-based models. In particular, we consider part-based models that exploit rich pairwise relationship between parts, e.g. the color symmetry between left/right limbs. This poses a computational challenge since the state space of each part is very large, and algorithmic tricks (e.g. the distance transform)(More)
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