Moving Object Detection Using Deep Learning Method

Authors

  • Tamanna Sahoo Department of Electronics and Communication Engineering, Institute of Technical Education and Research, Siksha ‘O’ Anusandhan Deemed to be University, Bhubaneswar, Odisha, India
  • Bibhuprasad Mohanty Department of Electronics and Communication Engineering, Institute of Technical Education and Research, Siksha ‘O’ Anusandhan Deemed to be University, Bhubaneswar, Odisha, India
  • Binod Kumar Pattanayak Department of Computer Science and Engineering, Institute of Technical Education and Research, Siksha ‘O’ Anusandhan Deemed to be University, Bhubaneswar, Odisha, India

Keywords:

Traffic surveillance, real-time, CNN and Softmax

Abstract

Moving object detection and tracking is the core traffic surveillance technology (TSS) . It is quite difficult to distinguish some things in a video frame series due to moving object's orientation fluctuation, changing weather circumstances, moving objects' appearance, and non-target objects in the background. Even though numerous techniques for detecting and following moving objects were developed, they were unable to produce the desired results. In this study, we use a deep learning method and trained models to create and deploy real-time object detection systems. Real-time static and moving object detection and object class recognition are capabilities of the system. The main objectives of this study were to examine and create a real-time object detection system using CNN. Since CNN has the limitation of rapid accuracy degradation after being saturated, the Softmax classifier is used in the stack layer for mitigating such performance decay. The simulation results of three video sequence of CDnet database shows almost 90% of average precision, with acceptable visual accuracy of moving object detection.

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Published

27.12.2023

How to Cite

Sahoo, T. ., Mohanty, B. ., & Pattanayak, B. K. . (2023). Moving Object Detection Using Deep Learning Method. International Journal of Intelligent Systems and Applications in Engineering, 12(9s), 282–290. Retrieved from https://ijisae.org/index.php/IJISAE/article/view/4275

Issue

Vol. 12 No. 9s (2024)

Section

Research Article

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