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基于轨迹系数特征空间表示法的含有异常情况的自动运动学习

KHALID Shehzad NAFTEL Andrew

KHALID Shehzad, NAFTEL Andrew. 基于轨迹系数特征空间表示法的含有异常情况的自动运动学习. 自动化学报, 2010, 36(5): 655-666. doi: 10.3724/SP.J.1004.2010.00655
引用本文: KHALID Shehzad, NAFTEL Andrew. 基于轨迹系数特征空间表示法的含有异常情况的自动运动学习. 自动化学报, 2010, 36(5): 655-666. doi: 10.3724/SP.J.1004.2010.00655
KHALID Shehzad, NAFTEL Andrew. Automatic Motion Learning in the Presence of Anomalies Using Coefficient Feature Space Representation of Trajectories. ACTA AUTOMATICA SINICA, 2010, 36(5): 655-666. doi: 10.3724/SP.J.1004.2010.00655
Citation: KHALID Shehzad, NAFTEL Andrew. Automatic Motion Learning in the Presence of Anomalies Using Coefficient Feature Space Representation of Trajectories. ACTA AUTOMATICA SINICA, 2010, 36(5): 655-666. doi: 10.3724/SP.J.1004.2010.00655

基于轨迹系数特征空间表示法的含有异常情况的自动运动学习

doi: 10.3724/SP.J.1004.2010.00655
详细信息
    通讯作者:

    KHALID Shehzad

Automatic Motion Learning in the Presence of Anomalies Using Coefficient Feature Space Representation of Trajectories

More Information
    Corresponding author: KHALID Shehzad
  • 摘要: Techniques for understanding video object motion activity are becoming increasingly important with the widespread adoption of CCTV surveillance systems. Motion trajectories provide rich spatiotemporal information about an object's activity. This paper presents a novel technique for clustering of object trajectory-based video motion clips using basis function approximations. Motion cues can be extracted using a tracking algorithm on video streams from video cameras. In the proposed system, trajectories are treated as time series and modelled using orthogonal basis function representation. Various function approximations have been compared including least squares polynomial, Chebyshev polynomials, piecewise aggregate approximation, discrete Fourier transform (DFT), and modified DFT (DFT-MOD). A novel framework, namely iterative hierarchical semi-agglomerative clustering using learning vector quantization (Iterative HSACT-LVQ), is proposed for learning of patterns in the presence of significant number of anomalies in training data. In this context, anomalies are defined as atypical behavior patterns that are not represented by sufficient samples in training data and are infrequently occurring or unusual. The proposed algorithm does not require any prior knowledge about the number of patterns hidden in unclassified dataset. Experiments using complex real-life trajectory datasets demonstrate the superiority of our proposed Iterative HSACT-LVQ-based motion learning technique compared to other recent approaches.
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出版历程
  • 收稿日期:  2008-12-29
  • 修回日期:  2009-05-15
  • 刊出日期:  2010-05-20

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