计算机应用 ›› 2016, Vol. 36 ›› Issue (6): 1677-1681.DOI: 10.11772/j.issn.1001-9081.2016.06.1677

• 虚拟现实与数字媒体 • 上一篇    下一篇

基于视皮层V1模型的随机点视频序列运动特征提取

邹洪中, 许悦雷, 马时平, 李帅, 张文达   

  1. 空军工程大学 航空航天工程学院, 西安 710038
  • 收稿日期:2015-11-09 修回日期:2016-01-20 出版日期:2016-06-10 发布日期:2016-06-08
  • 通讯作者: 邹洪中
  • 作者简介:邹洪中(1992-),男,四川达州人,硕士研究生,主要研究方向:模式识别、人工智能;许悦雷(1975-),男,河北辛集人,教授,博士,主要研究方向:图像处理、模式识别;马时平(1976-),男,四川绵竹人,副教授,博士,主要研究方向:图像处理、模式识别;李帅(1988-),男,河南新乡人,博士研究生,主要研究方向:模式识别、人工智能;张文达(1991-),男,山东淄博人,硕士研究生,主要研究方向:模式识别、人工智能。
  • 基金资助:
    国家自然科学基金资助项目(61372167,61379104)。

Motion feature extraction of random-dot video sequences based on V1 model of visual cortex

ZOU Hongzhong, XU Yuelei, MA Shiping, LI Shuai, ZHANG Wenda   

  1. Institute of Aeronautics and Astronautics Engineering, Air Force Engineering University, Xi'an Shaanxi 710038, China
  • Received:2015-11-09 Revised:2016-01-20 Online:2016-06-10 Published:2016-06-08
  • Supported by:
    This work is partially supported by the National Natural Science Foundation of China (61372167, 61379104).

摘要: 针对复杂场景中视频序列目标运动特征提取困难的问题,借鉴生物视觉系统对视频动态目标的运动感知机制,改进初级视皮层(V1)细胞模型,提出一种基于生物视皮层机制的视频运动特征提取方法。采用时空滤波器与半平方加归一化分别模拟神经元感受野的线性与非线性特性,再通过在输出权值中加入方向选择性调节参数得到普适性的V1细胞模型,从而解决传统模型方向选择性单一、多方向选择能力偏弱的问题。仿真结果表明所提模型模拟输出与生物实验数据较为吻合,能够模拟不同方向选择性的V1细胞,对复杂运动形态的随机点视频序列具有良好的运动特征提取能力。依靠该方法可以为处理特征光流信息提供新的思路,进而实现对视频序列目标的运动特征提取和有效跟踪。

关键词: 视频序列, 运动特征, V1细胞模型, 特征光流, 方向选择性

Abstract: Focusing on the issue of target motion feature extraction of video sequences in complex scene, and referring to the motion perception of biological vision system to the moving video targets, the traditional primary Visual cortex (V1) cell model of visual cortex was improved and a novel method of random-dot motion feature extraction based on the mechanism of biological visual cortex was proposed. Firstly, the spatial-temporal filter and half-squaring operation combined with normalization were adopted to simulate the linearity and nonlinearity of neuron's receptive field. Then, a universal V1 cell model was obtained by adding a directional selectivity adjustable parameter to the output weight, which solved the problem of the single direction selectivity and the disability to respond correctly to multi-direction motion in the traditional model. The simulation results show that the analog outputs of proposed model are almost consistent with the experimental data of biology, which indicates that the proposed model can simulate the V1 neurons of different direction selectivity and extract motion features well from random-dot video sequences with complex motion morphs. The proposed method can provide new idea for processing feature information of optical flow, extract motion feature of video sequence and track its object effectively.

Key words: video sequence, motion feature, V1 cell model, feature optical flow, direction selectivity

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