Object Trajectory-Based Activity Classification and Recognition Using Hidden Markov Models Export

@article{citeulike:1399421, abstract = {<para> Motion trajectories provide rich spatiotemporal information about an object's activity. This paper presents novel classification algorithms for recognizing object activity using object motion trajectory. In the proposed classification system, trajectories are segmented at points of change in curvature, and the subtrajectories are represented by their principal component analysis (PCA) coefficients. We first present a framework to robustly estimate the multivariate probability density function based on PCA coefficients of the subtrajectories using Gaussian mixture models (GMMs). We show that GMM-based modeling alone cannot capture the temporal relations and ordering between underlying entities. To address this issue, we use hidden Markov models (HMMs) with a data-driven design in terms of number of states and topology (e.g., left-right versus ergodic). Experiments using a database of over 5700 complex trajectories (obtained from UCI-KDD data archives and Columbia University Multimedia Group) subdivided into 85 different classes demonstrate the superiority of our proposed HMM-based scheme using PCA coefficients of subtrajectories in comparison with other techniques in the literature. </para>}, author = {Bashir, F. I. and Khokhar, A. A. and Schonfeld, D.}, citeulike-article-id = {1399421}, citeulike-linkout-0 = {http://dx.doi.org/10.1109/TIP.2007.898960}, citeulike-linkout-1 = {http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=4237188}, doi = {10.1109/TIP.2007.898960}, journal = {Image Processing, IEEE Transactions on}, keywords = {ccitt, classification, detection, hmm, trajectory}, number = {7}, pages = {1912--1919}, posted-at = {2008-05-15 00:50:52}, priority = {2}, title = {Object Trajectory-Based Activity Classification and Recognition Using Hidden Markov Models}, url = {http://dx.doi.org/10.1109/TIP.2007.898960}, volume = {16}, year = {2007} }

TY - JOUR ID - citeulike:1399421 L3 - citeulike-article-id:1399421 N2 - <para> Motion trajectories provide rich spatiotemporal information about an object's activity. This paper presents novel classification algorithms for recognizing object activity using object motion trajectory. In the proposed classification system, trajectories are segmented at points of change in curvature, and the subtrajectories are represented by their principal component analysis (PCA) coefficients. We first present a framework to robustly estimate the multivariate probability density function based on PCA coefficients of the subtrajectories using Gaussian mixture models (GMMs). We show that GMM-based modeling alone cannot capture the temporal relations and ordering between underlying entities. To address this issue, we use hidden Markov models (HMMs) with a data-driven design in terms of number of states and topology (e.g., left-right versus ergodic). Experiments using a database of over 5700 complex trajectories (obtained from UCI-KDD data archives and Columbia University Multimedia Group) subdivided into 85 different classes demonstrate the superiority of our proposed HMM-based scheme using PCA coefficients of subtrajectories in comparison with other techniques in the literature. </para> IS - 7 JF - Image Processing, IEEE Transactions on EP - 1919 TI - Object Trajectory-Based Activity Classification and Recognition Using Hidden Markov Models VL - 16 SP - 1912 KW - ccitt KW - classification KW - detection KW - hmm KW - trajectory AU - Bashir, FI AU - Khokhar, AA AU - Schonfeld, D PY - 2007/// UR - http://dx.doi.org/10.1109/TIP.2007.898960 ER -