Fast Human Detection Using a Cascade of Histograms of Oriented Gradients
Abstract
We integrate the cascade-of-rejectors approach with the Histograms of Oriented Gradients (HoG) features to achieve a fast and accurate human detection system. The features used in our system are HoGs of variable-size blocks that capture salient features of humans automatically. Using AdaBoost for feature selection, we identify the appropriate set of blocks, from a large set of possible blocks. In our system, we use the integral image representation and a rejection cascade which significantly speed up the computation. For a 320 × 280 image, the system can process 5 to 30 frames per second depending on the density in which we scan the image, while maintaining an accuracy level similar to existing methods.
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Integral HOG map has been used in pedestrian detection and has achieved dozens of times acceleration without losing any accuracy.
“Integral HOG map has been used in pedestrian detection and has achieved dozens of times' acceleration without losing any accuracy [124].”
From Object Detection in 20 Years: A Survey (2023) · cites this paper as [124]
The most commonly used idea to reduce feature computational redundancy is to compute the feature map of the whole image only once, which has achieved tens or even hundreds of times of acceleration.
“The most commonly used idea to reduce the feature computational redundancy is to compute the feature map of the whole image only once [18, 19, 124], which have achieved tens or even hundreds of times of acceleration.”
From Object Detection in 20 Years: A Survey (2023) · cites this paper as [124]
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 [124]
Cascaded detection has been especially applied to detection tasks of small objects in large scenes, e.g., face detection and pedestrian detection.
“In recent years, cascaded detection has been especially applied to those detection tasks of 'small objects in large scenes', e.g., face detection [126, 127], pedestrian detection [101, 124, 128], etc.”
From Object Detection in 20 Years: A Survey (2023) · cites this paper as [124]
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- Authors
- Zhu, Q., Yeh, M.-C., Cheng, K.-T., & Avidan, S.
- Affiliations
- University of California, Santa Barbara (US)
- Venue
- 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2 (CVPR'06)
- Publisher
- IEEE
- Published
- 2006-07-10
- Volume
- 2
- Pages
- 1491-1498
- ISSN
- 1063-6919 (ISSN-L)
- OpenAlex
- W2099355420
- Cited by
- 1,399
- References
- 15
- Citations by year
- 2026: 2 · 2025: 6 · 2024: 27 · 2023: 32 · 2022: 34 · 2021: 54 · 2020: 55 · 2019: 79 · 2018: 107 · 2017: 83 · 2016: 83 · 2015: 103 · 2014: 117 · 2013: 126 · 2012: 106
- FWCI
- 48.42
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings (publishedVersion)
- Rare & Special e-Zone (The Hong Kong University of Science and Technology) (submittedVersion)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Video Surveillance and Tracking Methods
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Human Pose and Action Recognition
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
- Keywords
- human detection, histogram of oriented gradients, AdaBoost, integral image, feature selection, real-time object detection
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