自动化学报Issue(8)：1644-1653,10.DOI:10.3724/SP.J.1004.2014.01644
单目视觉下目标三维行为的时间尺度不变建模及识别
Time-scale Invariant Modeling and Classifying for Object Behaviors in 3D Space Based on Monocular Vision
王蒙 1戴亚平 2王庆林1
作者信息
- 1. 北京理工大学自动化学院 北京 100081
- 2. 大理学院数学与计算机学院 大理 671003
- 折叠
摘要
Abstract
We present an approach to classify 3D behaviors online under monocular vision. We estimate similarity transformation between frames by matched markers, then transforms the similarity matrixes to logarithmic space to generate unified parameter sequence with 4 degrees of freedom. To eliminate the sensitivity of duration time, we formulate a time-scale invariant feature (TSIF) based on polygonal approximation algorithm, and implement online feature picking-up with dynamic programming. In the recognition phase, we use dynamic time warping to train the behavior templates with limited categories then recognize the test sequences. The experimental results show that the class separability of the proposed behavior template is increased by at least 60% to the comparative approaches, furthermore, recognizing unknown behaviors in continuous video online is achieved.关键词
三维重构/姿态估计/时间尺度不变特征/模板匹配/行为识别Key words
3D reconstruction/posture estimation/time-scale invariant feature (TSIF)/template matching/behavior recognition引用本文复制引用
王蒙,戴亚平,王庆林..单目视觉下目标三维行为的时间尺度不变建模及识别[J].自动化学报,2014,(8):1644-1653,10.
