Pedestrian detection is widely applied in driver assistance systems and video surveillance fields

while proposal generation is a significant preliminary work for pedestrian recognition and tracking.This paper proposes a method for fast online proposal generation using Local Mixture Probability (LMP) model.Poisson model and Gaussian model are separately established for online learning location and scale of pedestrians after region-dependent segmentation according to local similarity.Based on learning and updating models

both the probability of pedestrians occurrence and the probability distribution of the scale in specific regions can be obtained

which provides bases for pedestrian proposal generation and avoids searching blindness.Experiments on Caltech Pedestrian dataset show that LMP can achieve higher recall by fewer pedestrian detection proposals.