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A field model for human detection and tracking
2006
IEEE Transactions on Pattern Analysis and Machine Intelligence
The large shape variability and partial occlusions challenge most object detection and tracking methods for nonrigid targets such as pedestrians. This paper presents a new approach based on a two-layer statistical field model that characterizes the prior of the complex shape variations as a Boltzmann distribution and embeds this prior and the complex image likelihood into a Markov field. A probabilistic variational analysis of this model reveals a set of fixed-point equations characterizing the
doi:10.1109/tpami.2006.87
pmid:16640261
fatcat:vjreix5onjg7nasrh2czsffzfq