Hybrid human detection and recognition in surveillance
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AbstractIn this paper, we present a hybrid human recognition system for surveillance. A Cascade Head-Shoulder Detector (CHSD) with human body model is proposed to find the face region in a surveillance video frame image. The CHSD is a chain of rejecters which combines the advantages of Haar-like feature and HoG feature to make the detector more efficient and effective. For human recognition, we introduce an Overlapping Local Phase Feature (OLPF) to describe the face region, which can improve the robustness to pose change and blurring. To well model the variations of faces, an Adaptive Gaussian Mixture Model (AGMM) is presented to describe the distributions of the face images. Since AGMM does not need the facial topology, the proposed method is resistant to face detection error caused by imperfect localization or misalignment. Experimental results demonstrate the effectiveness of the proposed method in public dataset as well as real surveillance video. (C) 2016 Elsevier B.V. All rights reserved.
All Author(s) ListLiu Q, Zhang W, Li HL, Ngan KN
Journal nameNeurocomputing
Year2016
Month6
Day19
Volume Number194
PublisherELSEVIER SCIENCE BV
Pages10 - 23
ISSN0925-2312
eISSN1872-8286
LanguagesEnglish-United Kingdom
KeywordsAdaBoost; Gaussian Mixture Model; Head-Shoulder Detector; Human recognition; Overlapping Local Phase Feature; Surveillance
Web of Science Subject CategoriesComputer Science; Computer Science, Artificial Intelligence

Last updated on 2020-12-10 at 00:28