An HOG-LBP human detector with partial occlusion handling
Abstract
By combining Histograms of Oriented Gradients (HOG) and Local Binary Pattern (LBP) as the feature set, we propose a novel human detection approach capable of handling partial occlusion. Two kinds of detectors, i.e., global detector for whole scanning windows and part detectors for local regions, are learned from the training data using linear SVM. For each ambiguous scanning window, we construct an occlusion likelihood map by using the response of each block of the HOG feature to the global detector. The occlusion likelihood map is then segmented by Mean-shift approach. The segmented portion of the window with a majority of negative response is inferred as an occluded region. If partial occlusion is indicated with high likelihood in a certain scanning window, part detectors are applied on the unoccluded regions to achieve the final classification on the current scanning window. With the help of the augmented HOG-LBP feature and the global-part occlusion handling method, we achieve a detection rate of 91.3% with FPPW= 10−6, 94.7% with FPPW= 10−5, and 97.9% with FPPW= 10−4on the INRIA dataset, which, to our best knowledge, is the best human detection performance on the INRIA dataset. The global-part occlusion handling method is further validated using synthesized occlusion data constructed from the INRIA and Pascal dataset.
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The integral image can also be used to speed up more general features in object detection, e.g., color histogram and gradient histogram.
“The integral image can also be used to speed up more general features in object detection, e.g., color histogram, gradient histogram [124, 157-159], etc.”
From Object Detection in 20 Years: A Survey (2023) · cites this paper as [157]
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- Authors
- Wang, X., Han, T. X., & Yan, S.
- Affiliations
- University of Missouri (US)
- National University of Singapore (SG)
- Venue
- 2009 IEEE 12th International Conference on Computer Vision
- Publisher
- IEEE
- Published
- 2009-09
- Pages
- 32-39
- OpenAlex
- W2548197316
- Cited by
- 1,727
- References
- 36
- Citations by year
- 2026: 14 · 2025: 29 · 2024: 48 · 2023: 62 · 2022: 65 · 2021: 87 · 2020: 94 · 2019: 112 · 2018: 145 · 2017: 160 · 2016: 157 · 2015: 177 · 2014: 171 · 2013: 162 · 2012: 103
- FWCI
- 65.74
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- IEEE/CVF International Conference on Computer Vision (ICCV) (publishedVersion)
- Funders
- Leonard Wood Institute
- Army Research Laboratory
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Video Surveillance and Tracking Methods
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Image and Video Retrieval Techniques
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
- Keywords
- human detection, partial occlusion, histogram of oriented gradients, local binary pattern, mean shift segmentation, linear support vector machine, INRIA dataset
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