Existing vision-based pedestrian detection methods encounter many flaws

such as high false and miss detection rates

low detection accuracy on partial occluded and small scale objects

etc.In this paper

we propose a pedestrian detection method combining textual and visual information together.First

we use a vision-based method to initially localize the candidate visual objects.Second

we analyze the text information to get the text mentions corresponding to the visual objects.Finally

we propose a Markov random field-based model to infer the coreference relations between the candidate visual objects and textual mentions

so that the visual and textual information can be fused efficiently.The experimental results on the Caltech pedestrian detection benchmark enriched with textual description information have shown that the proposed method can not only improve the pedestrian detection accuracy by combining textual information with visual information

but also outperform the baseline anaphora resolution model by combining visual information with textual information.