Single-pedestrian detection aided by two-pedestrian detection
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AbstractIn this paper, we address the challenging problem of detecting pedestrians who appear in groups. A new approach is proposed for single-pedestrian detection aided by two-pedestrian detection. A mixture model of two-pedestrian detectors is designed to capture the unique visual cues which are formed by nearby pedestrians but cannot be captured by single-pedestrian detectors. A probabilistic framework is proposed to model the relationship between the configurations estimated by single- and two-pedestrian detectors, and to refine the single-pedestrian detection result using two-pedestrian detection. The two-pedestrian detector can integrate with any single-pedestrian detector. Twenty-five state-of-the-art single-pedestrian detection approaches are combined with the two-pedestrian detector on three widely used public datasets: Caltech, TUD-Brussels, and ETH. Experimental results show that our framework improves all these approaches. The average improvement is 9 percent on the Caltech-Test dataset, 11 percent on the TUD-Brussels dataset and 17 percent on the ETH dataset in terms of average miss rate. The lowest average miss rate is reduced from 37 to percent on the Caltech-Test dataset, from 55 to 50 percent on the TUD-Brussels dataset and from 43 to 38 percent on the ETH dataset.
All Author(s) ListOuyang W., Zeng X., Wang X.
Journal nameIEEE Transactions on Pattern Analysis and Machine Intelligence
Volume Number37
Issue Number9
PublisherInstitute of Electrical and Electronics Engineers
Place of PublicationUnited States
Pages1875 - 1889
LanguagesEnglish-United Kingdom
Keywordscontextual information, discriminative model, human detection, object detection, Part based model, pedestrian detection

Last updated on 2020-21-11 at 01:29