Visual Attention-Based Object Detection in Cluttered Environments

被引:5
|
作者
Machado, Eduardo [1 ]
Carrillo, Ivan [1 ,2 ]
Collado, Miguel [1 ]
Chen, Liming [2 ]
机构
[1] Ingn & Soluc Informat, Dept Innovat & Technol, Seville, Spain
[2] De Montfort Univ, Sch Comp Sci & Informat, Leicester, Leics, England
关键词
D O I
10.1109/SmartWorld-UIC-ATC-SCALCOM-IOP-SCI.2019.00064
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
The study of human visual attention is considered a hot topic in the field of activity recognition, experimental psychology research and human computer interaction. The importance of detecting user objects of interest in real time is critical to provide accurate cues about the user intentions. However, current methods for visual attention extraction and object detection suffer from low performance when moving to ongoing condition. Inherent complexity of cluttered environments is considered the major barrier to achieve good performances. To address this challenge, we present a novel method that includes head-worn eye tracker and egocentric video. Our method exploits sliding window-based time series approach in conjunction with a Heuristic probabilistic function to analyse user fixations around potential object of interest in an egocentric video. We evaluate the proposed method using a new dataset annotated with user gaze data and object within a frame image. Our experimental results show that our approach can outperforms several state-of-the-art commonality visual attention-based object detection methods.
引用
收藏
页码:133 / 139
页数:7
相关论文
共 50 条
  • [1] Attention-based fusion factor in FPN for object detection
    Li, Yuancheng
    Zhou, Shenglong
    Chen, Hui
    [J]. APPLIED INTELLIGENCE, 2022, 52 (13) : 15547 - 15556
  • [2] Attention-based fusion factor in FPN for object detection
    Yuancheng Li
    Shenglong Zhou
    Hui Chen
    [J]. Applied Intelligence, 2022, 52 : 15547 - 15556
  • [3] Where and What: Driver Attention-based Object Detection
    Rong, Yao
    Kassautzki, Naemi-Rebecca
    Fuhl, Wolfgang
    Kasneci, Enkelejda
    [J]. Proceedings of the ACM on Human-Computer Interaction, 2022, 6 (ETRA)
  • [4] Visual attention-based deepfake video forgery detection
    Ganguly, Shreyan
    Mohiuddin, Sk
    Malakar, Samir
    Cuevas, Erik
    Sarkar, Ram
    [J]. PATTERN ANALYSIS AND APPLICATIONS, 2022, 25 (04) : 981 - 992
  • [5] Visual attention-based deepfake video forgery detection
    Shreyan Ganguly
    Sk Mohiuddin
    Samir Malakar
    Erik Cuevas
    Ram Sarkar
    [J]. Pattern Analysis and Applications, 2022, 25 : 981 - 992
  • [6] Attention-based scale sequence network for small object detection
    Lee, Young-Woon
    Kim, Byung-Gyu
    [J]. HELIYON, 2024, 10 (12)
  • [7] Object Detection in Aerial Images with Attention-based Regression Loss
    Doloriel, Chandler Timm C.
    Cajote, Rhandley D.
    [J]. PROCEEDINGS OF 2022 ASIA-PACIFIC SIGNAL AND INFORMATION PROCESSING ASSOCIATION ANNUAL SUMMIT AND CONFERENCE (APSIPA ASC), 2022, : 1187 - 1191
  • [8] Object detection and tracking based on visual attention
    Zhang, Huawei
    Zhang, Qiaorong
    [J]. ICIC Express Letters, 2012, 6 (10): : 2667 - 2671
  • [9] Reverse Attention-Based Residual Network for Salient Object Detection
    Chen, Shuhan
    Tan, Xiuli
    Wang, Ben
    Lu, Huchuan
    Hu, Xuelong
    Fu, Yun
    [J]. IEEE TRANSACTIONS ON IMAGE PROCESSING, 2020, 29 : 3763 - 3776
  • [10] Channel and spatial attention-based Siamese network for visual object tracking
    Tian, Shishun
    Chen, Zixi
    Chen, Bolin
    Zou, Wenbin
    Li, Xia
    [J]. JOURNAL OF ELECTRONIC IMAGING, 2021, 30 (03)