When pedestrian detection (PD) is implemented on a central processing unit (CPU), performing real-time processing using a classical sliding window is difficult. Therefore, an efficient proposal generation method is required. A new generation method, named additive kernel binarized normed gradient (AKBING), is proposed herein, and this method is applied to the PD for real-time implementation on a CPU. The AKBING is based on an additive kernel support vector machine (AKSVM) and is implemented using the binarized normed gradient. The proposed PD can operate in real time because all AKSVM computations are approximated via simple atomic operations. In the suggested kernelized proposal method, the popular features and a classifier are combined, and the method is tested on a Caltech Pedestrian dataset and KITTI dataset. The experimental results show that the detection system with the proposed method improved the speed with minor degradation in detection accuracy.
|Number of pages||13|
|Journal||IEEE Transactions on Intelligent Transportation Systems|
|Publication status||Published - 2020 Mar|
Bibliographical noteFunding Information:
Manuscript received March 25, 2018; revised November 29, 2018; accepted February 16, 2019. Date of publication April 4, 2019; date of current version February 28, 2020. This work was supported by the Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education, Science and Technology under Grant NRF-2016R1A2A2A05005301. The Associate Editor for this paper was N. Zheng. (Corresponding author: Euntai Kim.) The authors are with the School of Electrical and Electronic Engineering, Yonsei University, Seoul 120-749, South Korea (e-mail: firstname.lastname@example.org). Digital Object Identifier 10.1109/TITS.2019.2904836
© 2000-2011 IEEE.
All Science Journal Classification (ASJC) codes
- Automotive Engineering
- Mechanical Engineering
- Computer Science Applications