Journal
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
Volume 21, Issue 3, Pages 1216-1228Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TITS.2019.2904836
Keywords
Proposals; Feature extraction; Support vector machines; Microsoft Windows; Real-time systems; Additives; Kernel; Additive kernel; binarized normed gradient; Caltech Pedestrian dataset; KITTI dataset; detection proposals; pedestrian detection; LDCF
Categories
Funding
- Basic Science Research Program through the National Research Foundation of Korea (NRF) - Ministry of Education, Science and Technology [NRF-2016R1A2A2A05005301]
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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.
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