Paper Title
Object Detection and Tracking in Compressed Domain (2022)
Article Identifiers
Authors
Pranjal , Nipurn Bishnoi , Monika Kumari , Ritwik Rishu , Dr. Vijay Shukla
Keywords
CNN, TensorFlow, DCT, Deep learning.
Abstract
In this research paper we have described the method for detecting and tracking multiple objects in various scenarios. None of existing models have used the power of deep CNN, with the speedy development in deep learning, various powerful tools are introduced which are able to address the problems in existing models, we have focused on object detection and tracking along with some variations to improve the detection performance. Many previous models were trained on normal mpeg videos which were less efficient but our model will track the objects using DCT compressed videos which will increase the overall efficiency of the model. Moving objects gets detected using TensorFlow object detection API and a CNN based object tracking algorithm is used for tracking the motion of an object in different scenarios. For the validation live input or the path of input will be taken where objects will get detected.
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How To Cite (APA)
Pranjal, Nipurn Bishnoi, Monika Kumari, Ritwik Rishu, & Dr. Vijay Shukla (May-2022). Object Detection and Tracking in Compressed Domain (2022). INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT, 7(5), 750-754. https://ijnrd.org/papers/IJNRD2205082.pdf
Issue
Volume 7 Issue 5, May-2022
Pages : 750-754
Other Publication Details
Paper Reg. ID: IJNRD_181314
Published Paper Id: IJNRD2205082
Downloads: 000121981
Research Area: Engineering
Country: Ghaziabad, Uttar Pradesh, India
Published Paper PDF: https://ijnrd.org/papers/IJNRD2205082.pdf
Published Paper URL: https://ijnrd.org/viewpaperforall?paper=IJNRD2205082
About Publisher
Journal Name: INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT(IJNRD)
ISSN: 2456-4184 | IMPACT FACTOR: 8.76 Calculated By Google Scholar | ESTD YEAR: 2016
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Licence
This work is licensed under a Creative Commons Attribution 4.0 International License and The Open Definition
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