Condensed Object Representation with Corner HOG Features for Object Classification in Outdoor Scenes

被引:0
|
作者
Yu, Tin Tin [1 ]
War, Nu [2 ]
机构
[1] Univ Comp Studies, Mandalay, Myanmar
[2] Comp Univ, Mandalay, Myanmar
关键词
HOG (Histogram of Gradient); Object Tracking; Action Recognition;
D O I
暂无
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
Nowadays, HOG (Histogram of Gradient) feature is extracted from the objects and using it in the classification tasks among the many visual application systems such as object tracking, action recognition and automated video surveillance. Most techniques of extraction HOG feature are based on cells and blocks. Although the HOG feature on cell and block are being robust for current visual systems, the alternative way to extract HOG feature that focus on corner points are presented in this paper. HOG features on corner points is extracted for multiple object detection system in which single or multiple moving objects are classified and labeled. And also comparison results on outdoor challenging sequences for HOG feature extraction on blocks and corners are provided with experimental results.
引用
收藏
页码:77 / 82
页数:6
相关论文
共 50 条
  • [41] Object representation with local features in geodesic distance space
    Slot, Krzysztof
    Gozdzik, Marek
    PROCEEDINGS OF THE SEVENTH IASTED INTERNATIONAL CONFERENCE ON VISUALIZATION, IMAGING, AND IMAGE PROCESSING, 2007, : 265 - +
  • [42] Object Categorization via Sparse Representation of Local Features
    Wang, Jin
    Sun, Xiangping
    Chen, Ronghua
    She, Mary
    Wang, Qiang
    2012 21ST INTERNATIONAL CONFERENCE ON PATTERN RECOGNITION (ICPR 2012), 2012, : 3005 - 3008
  • [43] Conceptual representation in the object classification task:: verification of the model
    Plichtová, J
    CESKOSLOVENSKA PSYCHOLOGIE, 1998, 42 (05): : 407 - 428
  • [44] Kernel Homotopy Based Sparse Representation For Object Classification
    Kang, Cuicui
    Liao, Shengcai
    Xiang, Shiming
    Pan, Chunhong
    2012 21ST INTERNATIONAL CONFERENCE ON PATTERN RECOGNITION (ICPR 2012), 2012, : 1479 - 1482
  • [45] PLSA-based Sparse Representation for Object Classification
    Yan, Yilin
    Hsieh, Jun-Wei
    Chiang, Hui-Fen
    Cheng, Shyi-C.
    Chen, Duan-yu
    2014 22ND INTERNATIONAL CONFERENCE ON PATTERN RECOGNITION (ICPR), 2014, : 1295 - 1300
  • [46] Object classification using a fragment-based representation
    Ullman, S
    Sali, E
    BIOLOGICALLY MOTIVATED COMPUTER VISION, PROCEEDING, 2000, 1811 : 73 - 87
  • [47] Gabor Filter based Image Representation for Object Classification
    Rizvi, Syed Tahir Hussain
    Cabodi, Gianpiero
    Gusmao, Pedro
    Francini, Gianluca
    2016 INTERNATIONAL CONFERENCE ON CONTROL, DECISION AND INFORMATION TECHNOLOGIES (CODIT), 2016, : 628 - 632
  • [48] Neural networks for object classification in a knowledge representation model
    Bassolet, CG
    Simonet, A
    Simonet, M
    CARI'96 - PROCEEDINGS OF THE 3RD AFRICAN CONFERENCE ON RESEARCH IN COMPUTER SCIENCE, 1996, : 145 - 155
  • [49] Shape Sparse Representation for Joint Object Classification and Segmentation
    Chen, Fei
    Yu, Huimin
    Hu, Roland
    IEEE TRANSACTIONS ON IMAGE PROCESSING, 2013, 22 (03) : 992 - 1004
  • [50] CNN-Based Object Detection via Segmentation Capabilities in Outdoor Natural Scenes
    Naseer, Aysha
    Al Mudawi, Naif
    Abdelhaq, Maha
    Alonazi, Mohammed
    Alazeb, Abdulwahab
    Algarni, Asaad
    Jalal, Ahmad
    IEEE ACCESS, 2024, 12 : 84984 - 85000