Pedestrian Proposal Generation Based on Local Mixture Probability Model

doi:10.3969/j.issn.0372-2112.2018.07.025

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ACTA ELECTRONICA SINICA ›› 2018, Vol. 46 ›› Issue (7) : 1719-1725. DOI: 10.3969/j.issn.0372-2112.2018.07.025

Pedestrian Proposal Generation Based on Local Mixture Probability Model

QIN Jian, XIAO Ting

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Abstract

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.

Key words

machine vision / pedestrian detection / LMP model / proposal generation

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QIN Jian, XIAO Ting. Pedestrian Proposal Generation Based on Local Mixture Probability Model[J]. Acta Electronica Sinica, 2018, 46(7): 1719-1725. https://doi.org/10.3969/j.issn.0372-2112.2018.07.025

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Funding

Program supported by Ministry of Education of China and China Mobile Research Fund (No.MCM20150102); Innovation Team Construction Project of Chongqing Municipal Universities (No.CXTDX201601006)