PERSON IDENTIFICATION AND TRACKING UING DEEP LEARNING

ELECTRONICS AND COMMUNICATION ENGINEERING

In the present scenario, digital data generation, data consumption becoming necessary due to advancement in technology. The human data processing becoming important in various types of applications like person authentication, verifications automatically by the machines. One of the applications is to identify the person automatically is by the machine. Numerous computer vision, machine and deep learning–based methods have been created in recent years. Majority of these methods are based on frontal view images/video sequences. The advancement of convolutional neural network reforms the way of object tracking.Person identification and tracking are critical tasks in many applications, such as security systems, video surveillance, and social media platforms. Deep learning has revolutionized the field of computer vision, enabling high accuracy in person identification and tracking. This paper provides an overview of person identification and tracking using deep learning techniques, such as Convolutional Neural Networks (CNNs). We discuss various deep learning architectures for person identification and tracking, including face recognition and body posture analysis. Finally, we suggest future directions for research in this field, such as multi-modal person identification and tracking and ethical considerations.

This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.