| JIA Chen,ZHANG Yao,SHI Fan,ZHAO Meng.Light field imaging based on a parallel SVM method for recognizing 2D fake pedestrians[J].Optoelectronics Letters,2022,(1):48-53 |
| Light field imaging based on a parallel SVM method for recognizing 2D fake pedestrians |
| Author Name | Affiliation | | JIA Chen | Key Laboratory of Computer Vision and System of Ministry of Education, Tianjin Key Laboratory of Intelligence Computing and Novel Software Technology, Tianjin University of Technology, Tianjin 300384, China | | ZHANG Yao | Key Laboratory of Computer Vision and System of Ministry of Education, Tianjin Key Laboratory of Intelligence Computing and Novel Software Technology, Tianjin University of Technology, Tianjin 300384, China | | SHI Fan | Key Laboratory of Computer Vision and System of Ministry of Education, Tianjin Key Laboratory of Intelligence Computing and Novel Software Technology, Tianjin University of Technology, Tianjin 300384, China
The MOE Key Laboratory of Weak-Light Nonlinear Photonics, Nankai University, Tianjin 300457, China | | ZHAO Meng | Key Laboratory of Computer Vision and System of Ministry of Education, Tianjin Key Laboratory of Intelligence Computing and Novel Software Technology, Tianjin University of Technology, Tianjin 300384, China |
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| Abstract: |
| It is novel to apply three-dimensional (3D) light field imaging technology to recognize two-dimensional (2D) fake pedestrians. In this paper, we propose a parallel support vector machine (SVM) method based on 3D light field imaging (light field camera) and machine learning techniques. A light field (LF) camera with robust sensors, which is able to record rich 3D information, is used as hardware equipment. Histogram of oriented gradient (HOG) feature extraction algorithm and SVM classification method are used to recognize the real and 2D fake pedestrians efficiently. Besides, we carry out an experiment on our improved LF pedestrian dataset. The experimental results of parameter optimization study show that in the case of 400 training samples (200 positive samples and 200 negative samples), 120 to 420 testing samples, and an HOG cellsize as 8×8, the best recognition accuracy with polynomial kernel function is improved by more than 2% compared with the previous method. The best accuracy is 99.17%. Otherwise, the recognition accuracy of more than 98.00% will be obtained even under other experimental conditions. |
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