ACCELERATED LOCAL FEATURE EXTRACTION IN A REUSE SCHEME FOR EFFICIENT ACTION RECOGNITION

被引：0
|
作者：
Chen, Jia-Lin [1 ]
Lin, Zhi-Yi [1 ]
Wan, Yi-Chen [1 ]
Chen, Liang-Gee [1 ]
机构：
[1] Natl Taiwan Univ, Grad Inst Elect Engn, DSP IC Design Lab, Taipei, Taiwan
来源：
2016 IEEE INTERNATIONAL CONFERENCE ON IMAGE PROCESSING (ICIP) | 2016年
关键词：
Feature reuse; local feature extraction; partial feature extraction; action recognition; region of interest;
D O I：
暂无
中图分类号：
TM [电工技术]; TN [电子技术、通信技术];
学科分类号：
0808 ; 0809 ;
摘要：
In this paper, we propose an accelerated local feature extraction in a reuse scheme for action recognition. Most local features of the previous frame could be reused due to the high correlation between successive frames. Feature extraction is only needed to be applied partially in the current frame. The full-frame features of the current frame are combined by features extracted at different times. Experimental results show that the proposed reuse scheme can achieve comparable performance to full-frame feature extraction while computational effort is reduced significantly.
引用
收藏
页码：296 / 299
页数：4
相关论文
共 50 条
  • [41] Local part model for action recognition
    Shi, Feng
    Laganiere, Robert
    Petriu, Emil
    IMAGE AND VISION COMPUTING, 2016, 46 : 18 - 28
  • [42] TRAJECTORY FEATURE FUSION FOR HUMAN ACTION RECOGNITION
    Megrhi, Sameh
    Beghdadi, Azeddine
    Souidene, Wided
    2014 5TH EUROPEAN WORKSHOP ON VISUAL INFORMATION PROCESSING (EUVIP 2014), 2014,
  • [43] Local and Global Feature Descriptors Combination from RGB-Depth Videos for Human Action Recognition
    Al-Akam, Rawya
    Paulus, Dietrich
    PROCEEDINGS OF THE 7TH INTERNATIONAL CONFERENCE ON PATTERN RECOGNITION APPLICATIONS AND METHODS (ICPRAM 2018), 2018, : 265 - 272
  • [44] SkeletonCapsuleNet: An Efficient Network for Action Recognition
    Yu, Yue
    Tian, Niehao
    Chen, Xiangru
    Li, Ying
    2018 8TH INTERNATIONAL CONFERENCE ON VIRTUAL REALITY AND VISUALIZATION (ICVRV), 2018, : 74 - 77
  • [45] MPEG CDVS Feature Trajectories for Action Recognition in Videos
    Dasari, Radhakrishna
    Chen, Chang Wen
    IEEE 1ST CONFERENCE ON MULTIMEDIA INFORMATION PROCESSING AND RETRIEVAL (MIPR 2018), 2018, : 301 - 304
  • [46] FEASE: Feature Selection and Enhancement Networks for Action Recognition
    Zhou, Lu
    Lu, Yuanyao
    Jiang, Haiyang
    NEURAL PROCESSING LETTERS, 2024, 56 (02)
  • [47] A New Relational Geometric Feature for Human Action Recognition
    Vinagre, M.
    Aranda, J.
    Casals, A.
    INFORMATICS IN CONTROL, AUTOMATION AND ROBOTICS, 2015, 325 : 263 - 278
  • [48] Action Recognition in a High-Dimensional Feature Space
    Adiguzel, Hande
    Erdem, Hayrettin
    Ferhatosmanoglu, Hakan
    Duygulu, Pinar
    2013 21ST SIGNAL PROCESSING AND COMMUNICATIONS APPLICATIONS CONFERENCE (SIU), 2013,
  • [49] Joint Feature Optimization and Fusion for Compressed Action Recognition
    Li, Hanhui
    Jiang, Xudong
    Guan, Boliang
    Tan, Raymond Rui Ming
    Wang, Ruomei
    Thalmann, Nadia Magnenat
    IEEE TRANSACTIONS ON IMAGE PROCESSING, 2021, 30 : 7926 - 7937
  • [50] Collaborative and Multilevel Feature Selection Network for Action Recognition
    Zheng, Zhenxing
    An, Gaoyun
    Cao, Shan
    Wu, Dapeng
    Ruan, Qiuqi
    IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS, 2023, 34 (03) : 1304 - 1318
12345