SciTePress - Affiliation Profile

Research.Publish.Connect.

*Please fill out at least one Field. *Value must be an number!

Title:
ISBN:
Year:
Acronym:
Subject:

Advanced Search Proceedings Search

If you're looking for an exact phrase use quotation marks on text fields.

*Please fill out at least one Field.

Title:
Author:
Affiliation:
Subject:

Advanced Search Papers Search

If you're looking for an exact phrase use quotation marks on text fields.

*Please fill out at least one Field.

Name:
Affiliation:
Country:
Conference:
Subject:

Advanced Search Authors Search

If you're looking for an exact phrase use quotation marks on text fields.

*Please fill out at least one Field.

Name:
Country:
Subject:

Advanced Search Affiliations Search

If you're looking for an exact phrase use quotation marks on text fields.

Proceedings

Proceedings Search *Please fill out at least one Field. *Value must be an number!

Title:
ISBN:
Year:
Acronym:
Subject:

Advanced Search Proceedings Search

If you're looking for an exact phrase use quotation marks on text fields.

Papers

Papers Search *Please fill out at least one Field.

Title:
Author:
Affiliation:
Subject:

Advanced Search Papers Search

If you're looking for an exact phrase use quotation marks on text fields.

Authors

Authors Search *Please fill out at least one Field.

Name:
Affiliation:
Country:
Conference:
Subject:

Advanced Search Authors Search

If you're looking for an exact phrase use quotation marks on text fields.

Advanced Search

Affiliation

Department of Radiology, The Netherlands Cancer Institute - Antoni van Leeuwenhoek Hospital, Amsterdam, The Netherlands

Context (please use complete words only)

Field

Sort by

1 Papers

A Lightweight Gaussian-Based Model for Fast Detection and Classification of Moving Objects

Topics: Object Detection and Localization; Optical Flow and Motion Analyses; Deep Learning for Visual Understanding

Authors: Joaquin Palma-Ugarte ¹ , Laura Estacio-Cerquin ^{2

;

3} , Victor Flores-Benites ⁴ and Rensso Mora-Colque ¹

Affiliations: ¹ Department of Computer Science, Universidad Católica San Pablo, Arequipa, Peru , ² Department of Radiology, The Netherlands Cancer Institute - Antoni van Leeuwenhoek Hospital, Amsterdam, The Netherlands , ³ GROW School for Oncology and Developmental Biology, Maastricht University, Maastricht, The Netherlands , ⁴ Universidad de Ingeniería y Tecnología – UTEC, Lima, Peru

Digital Object Identifier: 10.5220/0011697200003417

Proceedings of VISAPP 2023/VISIGRAPP, ISBN: 978-989-758-634-7

Quick Abstract

Moving object detection and classification are fundamental tasks in computer vision. However, current solutions detect all objects, and then another algorithm is used to determine which objects are in motion. Furthermore, diverse solutions employ complex networks that require a lot of computational resources, unlike lightweight solutions that could lead to widespread use. We introduce TRG-Net, a unified model that can be executed on computationally limited devices to detect and classify just moving objects. This proposal is based on the Faster R-CNN architecture, MobileNetV3 as a feature extractor, and a Gaussian mixture model for a fast search of regions of interest based on motion. TRG-Net reduces the inference time by unifying moving object detection and image classification tasks, and by limiting the regions of interest to the number of moving objects. Experiments over surveillance videos and the Kitti dataset for 2D object detection show that our approach improves the inference time of Faster R-CNN (0.221 to 0.138s) using fewer parameters (18.91 M to 18.30 M) while maintaining average precision (AP=0.423). Therefore, TRG-Net achieves a balance between precision and speed, and could be applied in various real-world scenarios.