A generalized EMD with body prior for pedestrian identification
In this paper, we present a generalized Earth Movers Distance (EMD) for pedestrian identification with body prior. (i) The general configuration of body is a valuable cue for human identification. A model of body prior about the general configuration is pursued to describe body prior for every pedestrian. In addition, the spatial incompatibility is computed by KullbackLeibler divergence according to the pursued model and embedded into the ground distance of EMD. (ii) Furthermore, we generalize EMD by assigning different weights to regions of body, which are learned based on maximum margin criterion to boost discriminative power for pedestrian identification. The experimental results show that the generalized EMD and body prior substantially improve performance on pedestrian identification and the proposed approach is comparable to the state-of-the-art performance.
generalized emd emd body prior pedestrian identification probabilistic map matching metric kl divergence learning weight