Abstract
In this paper, we propose a boosted 3-D PCA algorithm based on an efficient analysis method. The proposed method involves three steps that improve image detection. In the first step, the proposed method designs a new analysis method to solve the performance problem caused by data imbalance. In the second step, a parallel cross-validation structure is used to enhance the new analysis method further. We also design a modified AdaBoost algorithm to improve the detector accuracy performance of the new analysis method. In order to verify the performance of this system, we experimented with a benchmark dataset. The results show that the new analysis method is more efficient than other image detection methods.
Highlights
Analysis MethodFeatured Application: The proposed method is useful in applications where it is not sufficient to apply a traffic safety technology of high reliability
Introduction on Efficient Analysis MethodAppl.The rapid advancement of information and communications technology (ICT) has led to the active commercialization of the autonomous vehicle industry
In the new boosted 3-D PCA method structure, the AdaBoost algorithm is applied to each dataset segmented into K-parts, as shown in Figure 4, to extract the low-performance detection result at the K-th level
Summary
Featured Application: The proposed method is useful in applications where it is not sufficient to apply a traffic safety technology of high reliability. We designed a new method because the existing method is inappropriate for traffic safety techniques due to its low reliability middle inference process in deep learning
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
