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Earlier: A1, A2, A4, A5, Only:
Pedestrian Recognition Using Second-Order HOG Feature,
ACCV09(II: 628-634).
Springer DOI
0909
See also Road Image Segmentation and Recognition Using Hierarchical Bag-of-Textons Method.
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Approximate RBF kernel SVM and its applications in pedestrian
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MLMotion08(xx-yy).
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Pedestrian Detection by Using a Spatio-Temporal Histogram of Oriented
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Human Detection by Quadratic Classification on Subspace of Extended
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IP(23), No. 1, January 2014, pp. 287-297.
IEEE DOI
1402
BibRef
Earlier:
Extended Histogram of Gradients with Asymmetric Principal Component and
Discriminant Analyses for Human Detection,
CRV11(64-71).
IEEE DOI
1105
BibRef
Earlier:
Extended Histogram of Gradients feature for human detection,
ICIP10(3473-3476).
IEEE DOI
1009
gradient methods
BibRef
Kim, S.[Soojin],
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Efficient Pedestrian Detection Using Multi-Scale HOG Features with Low
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IEICE(E97-D), No. 2, February 2013, pp. 366-369.
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Cyber(44), No. 3, March 2014, pp. 342-354.
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1404
image classification
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1710
Cameras, Detectors, Feature extraction, Laser radar,
Object detection, Radio frequency, Multicue, multimodal,
multiview (MV), object, detection
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Marin, J.[Javier],
Vazquez, D.[David],
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Leibe, B.[Bastian],
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ICCV13(2592-2599)
IEEE DOI
1403
HOG; LBP; Local Experts; Pedestrian detection; Random Forest
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Chen, P.,
Huang, C.,
Lien, C.,
Tsai, Y.,
An Efficient Hardware Implementation of HOG Feature Extraction for
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ITS(15), No. 2, April 2014, pp. 656-662.
IEEE DOI
1404
Approximation methods
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Whytock, T.P.[Tenika P.],
Belyaev, A.[Alexander],
Robertson, N.M.[Neil M.],
Dynamic Distance-Based Shape Features for Gait Recognition,
JMIV(50), No. 3, November 2014, pp. 314-326.
Springer DOI
1410
BibRef
Earlier:
Towards Robust Gait Recognition,
ISVC13(II:523-531).
Springer DOI
1311
BibRef
And:
Improving Robustness and Precision in GEI + HOG Action Recognition,
ISVC13(I:119-128).
Springer DOI
1310
BibRef
Li, N.[Nijun],
Cheng, X.[Xu],
Zhang, S.F.[Suo-Fei],
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Cheng, X.[Xu],
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Liu, Y.F.[Yi-Feng],
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1411
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Hua, C.S.[Chun-Sheng],
Makihara, Y.S.[Yasu-Shi],
Yagi, Y.S.[Yasu-Shi],
Iwasaki, S.[Shun],
Miyagawa, K.[Keisuke],
Li, B.[Bo],
Onboard monocular pedestrian detection by combining spatio-temporal hog
with structure from motion algorithm,
MVA(26), No. 2-3, April 2015, pp. 161-183.
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1504
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Wu, S.[Si],
Laganière, R.[Robert],
Payeur, P.[Pierre],
Improving pedestrian detection with selective gradient
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PR(48), No. 8, 2015, pp. 2364-2376.
Elsevier DOI
1505
Pedestrian detection
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Wu, S.[Si],
Wang, S.,
Laganière, R.[Robert],
Liu, C.,
Wong, H.S.,
Xu, Y.,
Exploiting Target Data to Learn Deep Convolutional Networks for
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IP(27), No. 3, March 2018, pp. 1418-1432.
IEEE DOI
1801
Adaptation models, Data models, Detectors, Feature extraction,
Labeling, Training, Human detection, convolutional network,
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MVA(28), No. 1-2, February 2017, pp. 49-59.
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1702
Histogram of orientations
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Liu, B.Z.[Bao-Zhen],
Wu, H.[Hang],
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Sector-ring HOG for rotation-invariant human detection,
SP:IC(54), No. 1, 2017, pp. 1-10.
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1704
Rotation-invariant detection
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Liu, B.Z.[Bao-Zhen],
Wu, H.[Hang],
Su, W.H.[Wei-Hua],
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Bilal, M.,
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IEEE DOI
2003
Support vector machines, Detectors, Training, Feature extraction,
Kernel, Benchmark testing, Quantization (signal),
pedestrian detection
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Liu, X.,
Toh, K.,
Allebach, J.P.,
Pedestrian Detection Using Pixel Difference Matrix Projection,
ITS(21), No. 4, April 2020, pp. 1441-1454.
IEEE DOI
2004
Feature extraction, Detectors, Histograms, Shape,
Intelligent transportation systems, Video surveillance,
HOG
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Errami, M.[Mounir],
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An Efficient Pedestrian Detector Based on Saliency and HOG Features
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ISVC16(II: 101-107).
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1701
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Balasubramanian, P.,
Pathak, S.,
Mittal, A.,
Improving Gradient Histogram Based Descriptors for Pedestrian
Detection in Datasets with Large Variations,
Robust16(1177-1186)
IEEE DOI
1612
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Xu, Y.[Yuan],
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Xu, X.L.[Xiao-Liang],
Jiang, M.[Mei],
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A two-stage hog feature extraction processor embedded with SVM for
pedestrian detection,
ICIP15(3452-3455)
IEEE DOI
1512
FPGA
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Mahmoud, A.[Ali],
El-Barkouky, A.[Ahmed],
Graham, J.[James],
Farag, A.[Aly],
Pedestrian detection using mixed partial derivative based histogram
of oriented gradients,
ICIP14(2334-2337)
IEEE DOI
1502
Histogram of Oriented Gradients
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Miramontes-Jaramillo, D.[Daniel],
Kober, V.[Vitaly],
Díaz-Ramírez, V.H.[Víctor Hugo],
Rotation Invariant Tracking Algorithm Based on Circular HOGs,
MCPR15(115-124).
Springer DOI
1506
BibRef
Earlier:
A Robust Tracking Algorithm Based on HOGs Descriptor,
CIARP14(54-61).
Springer DOI
1411
BibRef
Earlier:
CWMA: Circular Window Matching Algorithm,
CIARP13(I:439-446).
Springer DOI
1311
BibRef
Beltrán-Herrera, A.,
Vázquez-Santacruz, E.,
Gamboa-Zuñiga, M.,
Real-Time Classification of Lying Bodies by HOG Descriptors,
MCPR14(211-220).
Springer DOI
1407
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Arie, M.[Makoto],
Shibata, M.[Masatoshi],
Terabayashi, K.[Kenji],
Moro, A.[Alessandro],
Umeda, K.[Kazunori],
Fast human detection using template matching for gradient images and
aSC descriptors based on subtraction stereo,
ICIP13(3118-3122)
IEEE DOI
1402
Human detection
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Takahisa, K.[Kishino],
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Micheletto, R.[Ruggero],
A Fast and Precise HOG-Adaboost Based Visual Support System Capable to
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ACVR13(20-29).
Springer DOI
1309
BibRef
Salas, Y.S.[Yainuvis Socarrás],
Bermudez, D.V.[David Vázquez],
López Peña, A.M.[Antonio M.],
Gomez, D.G.[David Gerónimo],
Gevers, T.[Theo],
Improving HOG with Image Segmentation: Application to Human Detection,
ACIVS12(178-189).
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1209
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Kittipanya-Ngam, P.[Panachit],
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HOG-Based Descriptors on Rotation Invariant Human Detection,
VS10(143-152).
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1109
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Geismann, P.[Philip],
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Speeding Up HOG and LBP Features for Pedestrian Detection by
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ISVC10(I: 243-252).
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1011
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Zeng, C.B.[Cheng-Bin],
Ma, H.D.[Hua-Dong],
Ming, A.[Anlong],
Fast human detection using mi-sVM and a cascade of HOG-LBP features,
ICIP10(3845-3848).
IEEE DOI
1009
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Dong, L.[Li],
Yu, X.G.[Xin-Guo],
Li, L.Y.[Li-Yuan],
Hoe, J.K.E.[Jerry Kah Eng],
HOG based multi-stage object detection and pose recognition for service
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ICARCV10(2495-2500).
IEEE DOI
1109
BibRef
Wang, X.Y.[Xioa-Yu],
Han, T.X.[Tony X.],
Yan, S.C.[Shui-Cheng],
An HOG-LBP Human Detector with Partial Occlusion Handling,
ICCV09(32-39).
IEEE DOI
0909
BibRef
Lillywhite, K.[Kirt],
Lee, D.J.[Dah-Jye],
Zhang, D.[Dong],
Real-time human detection using histograms of oriented gradients on a
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WACV09(1-6).
IEEE DOI
0912
See also feature construction method for general object recognition, A.
BibRef
Chuang, C.H.[Cheng-Hsiung],
Huang, S.S.[Shih-Shinh],
Fu, L.C.[Li-Chen],
Hsiao, P.Y.[Pei-Yung],
Monocular multi-human detection using Augmented Histograms of Oriented
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ICPR08(1-4).
IEEE DOI
0812
BibRef
Watanabe, T.[Tomoki],
Ito, S.[Satoshi],
Two Co-occurrence Histogram Features Using Gradient Orientations and
Local Binary Patterns for Pedestrian Detection,
ACPR13(415-419)
IEEE DOI
1408
application specific integrated circuits
BibRef
Watanabe, T.[Tomoki],
Ito, S.[Satoshi],
Yokoi, K.[Kentaro],
Co-occurrence Histograms of Oriented Gradients for Pedestrian Detection,
PSIVT09(37-47).
Springer DOI
0901
BibRef
Mu, Y.D.[Ya-Dong],
Yan, S.C.[Shui-Cheng],
Liu, Y.[Yi],
Huang, T.S.[Thomas S.],
Zhou, B.F.[Bing-Feng],
Discriminative local binary patterns for human detection in personal
album,
CVPR08(1-8).
IEEE DOI
0806
BibRef
Wang, C.C.R.[Chi-Chen Raxle],
Lien, J.J.J.[Jenn-Jier James],
AdaBoost Learning for Human Detection Based on Histograms of Oriented
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ACCV07(I: 885-895).
Springer DOI
0711
BibRef
Zhu, Q.A.[Qi-Ang],
Yeh, M.C.[Mei-Chen],
Cheng, K.T.[Kwang-Ting],
Avidan, S.[Shai],
Fast Human Detection Using a Cascade of Histograms of Oriented
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CVPR06(II: 1491-1498).
IEEE DOI
0606
BibRef
Dalal, N.[Navneet],
Triggs, B.[Bill],
Histograms of Oriented Gradients for Human Detection,
CVPR05(I: 886-893).
IEEE DOI
0507
Award, Longuet-Higgins.
BibRef
Chapter on Motion -- Feature-Based, Long Range, Motion and Structure Estimates, Tracking, Surveillance, Activities continues in
Motion Based Human Detection, Spatio-Temporal Analysis, Pedestrians .