Human head pose estimation based on HF method

被引:2
作者
Anitta, D. [1 ]
Fathima, Annis A. [1 ]
机构
[1] VIT Univ, Sch Elect Engn, Chennai, Tamil Nadu, India
关键词
Human motion tracking; Head pose; Pose estimation; Hough Transform; Random Forest;
D O I
10.1016/j.micpro.2020.103802
中图分类号
TP3 [计算技术、计算机技术];
学科分类号
0812 ;
摘要
This Paper addresses the problem of head pose estimation. Driving assistance technology utilizes head pose estimation as an indicator for visual focus and mental attention of the driver. Head pose estimation detects head orientation with respect to the camera. Model based and appearance-based methods are the two approaches in head pose estimation. The first approach uses the facial features to create a face geometrical models whereas the second method only takes into consideration the entire face image. The proposed appearance-based method work is performed using Hough transform and random forest to classify ninety-three classes of Hough values in order to find the exact head pose. The performance of the proposed work is evaluated based on accuracy and the time taken to detect the head pose. The paper outperforms many other previous works.
引用
收藏
页数:7
相关论文
共 33 条
[1]   Near Real-Time Three Axis Head Pose Estimation Without Training [J].
Abate, Andrea F. ;
Barra, Paola ;
Bisogni, Carmen ;
Nappi, Michele ;
Ricciardi, Stefano .
IEEE ACCESS, 2019, 7 :64256-64265
[2]   Exploration of Cervical Myelopathy Location From Somatosensory Evoked Potentials Using Random Forests Classification [J].
Cui, Hongyan ;
Wang, Yazhou ;
Li, Guangsheng ;
Huang, Yongcan ;
Hu, Yong .
IEEE TRANSACTIONS ON NEURAL SYSTEMS AND REHABILITATION ENGINEERING, 2019, 27 (11) :2254-2262
[3]  
Dang K, 2017, PROCEEDINGS OF THE 7TH INTERNATIONAL CONFERENCE ON CLOUD COMPUTING, DATA SCIENCE AND ENGINEERING (CONFLUENCE 2017), P629, DOI 10.1109/CONFLUENCE.2017.7943228
[4]  
Dong Huiying, 2015, 2015 27th Chinese Control and Decision Conference (CCDC), P4892, DOI 10.1109/CCDC.2015.7162800
[5]   Target Detection Based on Random Forest Metric Learning [J].
Dong, Yanni ;
Du, Bo ;
Zhang, Liangpei .
IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING, 2015, 8 (04) :1830-1838
[6]   Salient Object Detection via Random Forest [J].
Du, Shuze ;
Chen, Shifeng .
IEEE SIGNAL PROCESSING LETTERS, 2014, 21 (01) :51-54
[7]   Paraconsistent Random Forest: An Alternative Approach for Dealing With Uncertain Data [J].
Favieiro, Gabriela W. ;
Balbinot, Alexandre .
IEEE ACCESS, 2019, 7 :147914-147927
[8]   Random Forests for Regression as a Weighted Sum of k-Potential Nearest Neighbors [J].
Fernandez-Gonzalez, Pablo ;
Bielza, Concepcion ;
Larranaga, Pedro .
IEEE ACCESS, 2019, 7 :25660-25672
[9]   DPRF: A Differential Privacy Protection Random Forest [J].
Hou, Jun ;
Li, Qianmu ;
Meng, Shunmei ;
Ni, Zhen ;
Chen, Yini ;
Liu, Yaozong .
IEEE ACCESS, 2019, 7 :130707-130720
[10]   QuatNet: Quaternion-Based Head Pose Estimation With Multiregression Loss [J].
Hsu, Heng-Wei ;
Wu, Tung-Yu ;
Wan, Sheng ;
Wong, Wing Hung ;
Lee, Chen-Yi .
IEEE TRANSACTIONS ON MULTIMEDIA, 2019, 21 (04) :1035-1046