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 suffcient 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.
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 suffcient 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.
出处
《自动化学报》
EI
CSCD
北大核心
2010年第5期655-666,共12页
Acta Automatica Sinica
关键词
自动化
自动机械
CCTV
电视机
Object trajectory
dimensionality reduction
trajectory clustering
event mining
anomaly detection