Multi Moving Objects Detection in Video Using Pre-trained Deep Convolutional Neural Networks

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Title

Multi Moving Objects Detection in Video Using Pre-trained Deep Convolutional Neural Networks

Subject

Object Tracking; Video processing; Deep neural network; K-mean clustering.

Description

Abstract- Nowadays object tracking is a critical concern in the field of machine vision. With the advent of powerful computers, affordable cameras, and growing demand for automatic video analysis, researchers have shown significant interest in object tracking. Various methods have been proposed for tracking objects in machine vision, but a key challenge remains: ensuring the robustness of tracking algorithms across consecutive video frames. In recent years, deep neural networks have emerged as a promising approach for accurate position estimation. In this study, we propose an enhanced method that combines deep convolutional neural networks with established techniques like K-means clustering. Our approach addresses challenges such as object disappearances and severe displacements. The selection of deep neural networks is motivated by their compatibility with target identification in video sequences, and achieving a remarkably low error rate in tracking validates our claim.

Creator

Abolfazl Ansaripour, Hosein Mahvash Mohamadi,

Source

www.ijcit.com

Date

December 2024

Contributor

peri irawan

Format

pdf

Language

english

Type

text

Files

Citation

Abolfazl Ansaripour, Hosein Mahvash Mohamadi, , “Multi Moving Objects Detection in Video Using Pre-trained Deep Convolutional Neural Networks,” Repository Horizon University Indonesia, accessed June 5, 2025, https://repository.horizon.ac.id/items/show/9171.