Tracking People in Video Using Neural Network Features and Facial Identification Taking into Account the Mask Mode

Detection and tracking of people in video in distributed video surveillance systems is a difficult task, which has become even more complicated in the conditions of the mask mode, when some people may be wearing masks. To solve this problem, the paper proposes algorithms for detecting masked people...

Full description

Saved in:

Bibliographic Details
Published in:	Pattern recognition and image analysis Vol. 33; no. 2; pp. 208 - 216
Main Authors:	Shiping Ye, Kurnosov, I. L., Bohush, R. P., Ma, Guangdi, Weichen, Yang, Ablameyko, S. V.
Format:	Journal Article
Language:	English
Published:	Moscow Pleiades Publishing 01-06-2023 Springer Nature B.V
Subjects:	Algorithms Application Problems Computer Science Face recognition Image Processing and Computer Vision Masks Motion perception Neural networks Pattern Recognition Surveillance systems Tracking Trajectory analysis masked face recognition detection and tracking of people in video neural networks
Online Access:	Get full text
Tags:	Add Tag No Tags, Be the first to tag this record!

Description
Summary:	Detection and tracking of people in video in distributed video surveillance systems is a difficult task, which has become even more complicated in the conditions of the mask mode, when some people may be wearing masks. To solve this problem, the paper proposes algorithms for detecting masked people and further tracking them using facial recognition systems based on neural networks. To train a neural network to detect masked faces, an approach is proposed that involves applying masks to faces from existing data sets, which makes it possible to expand the training sample and increase the accuracy of recognition of masked faces. The features of masked faces are used to establish the correspondence of people in the frames. This makes it possible to increase the efficiency of detection and tracking upon hiding of people behind objects of the background, high similarity of external features of several people, and analysis of the trajectories of their movement. Examples of detection and tracking of people are shown and appropriate recommendations are given.
ISSN:	1054-6618 1555-6212
DOI:	10.1134/S1054661823020177