Feature extraction algorithm for fast moving pedestrians with frame drop constraint based on deep learning Online publication date: Tue, 22-Oct-2019
by Mei Ma; Yaomin Hu
International Journal of Information and Communication Technology (IJICT), Vol. 15, No. 4, 2019
Abstract: When the existing method extracts the information of the fast moving pedestrian, the frame dropping phenomenon may occur, resulting in low extraction precision. A fast moving pedestrian frame loss constrained feature extraction algorithm based on depth tilt is proposed. Block matching and denoising are performed on the pedestrian image. The contour feature extraction method is used to reconstruct the adjacent frames and the reconstructed image frame vector is sub-block fusion. The depth learning algorithm is used to extract the feature quantity of the gray pixel from the frame falling part of the image. Improved feature extraction algorithm for pedestrians with frame loss constraints. The simulation results show that the standard deviation of the frame loss of the extraction result is 8.235 and the standard deviation of the non-drop frame is 4.353. It proves that the algorithm has low frame loss rate and high extraction and recognition ability.
Existing subscribers:
Go to Inderscience Online Journals to access the Full Text of this article.
If you are not a subscriber and you just want to read the full contents of this article, buy online access here.Complimentary Subscribers, Editors or Members of the Editorial Board of the International Journal of Information and Communication Technology (IJICT):
Login with your Inderscience username and password:
Want to subscribe?
A subscription gives you complete access to all articles in the current issue, as well as to all articles in the previous three years (where applicable). See our Orders page to subscribe.
If you still need assistance, please email subs@inderscience.com
Our Blog 
Follow us on Twitter 
Visit us on Facebook