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Video-based Object Detection Using Voice Recognition and YoloV7

Authors:
Issa Abdoul Razac Djinko
Thabet Kacem

Keywords: Artificial Intelligence, Object Detection, Voice Recognition.

Abstract:
Artificial Intelligence (AI) developments in recent years have allowed several new types of applications to emerge. In particular, detecting people and objects from sequences of pictures or videos has been an exciting field of research. Even though there have been notable achievements with the emergence of sophisticated AI models, there needs to be a specialized research effort that helps people finding misplaced items from a set of video sequences. In this paper, we leverage voice recognition and Yolo (You Only Look Once) real-time object detection system to develop an AI-based solution that addresses this challenge. This solution assumes that previous recordings of the objects of interest and storing them in the dataset have already occurred. To find a misplaced object, the user delivers a voice command that is in turn fed into the Yolo model to detect where and when the searched object was seen last. The outcome of this process is a picture that is provided as evidence. We used Yolov7 for object detection thanks to its better accuracy and wider database while leveraging Google voice recognizer to translate the voice command into text. The initial results we obtained show a promising potential for the success of our approach. Our findings can be extended to be applied to various other scenarios ranging from detecting health risks for elderly people to assisting authorities in locating potential persons of interest.

Pages: 7 to 12

Copyright: Copyright (c) IARIA, 2023

Publication date: March 13, 2023

Published in: conference

ISSN: 2308-4065

ISBN: 978-1-68558-064-3

Location: Barcelona, Spain

Dates: from March 13, 2023 to March 17, 2023