Paper Info
Title
Abnormal Behavior Detection via Sparse Reconstruction Analysis of Trajectory
Abstract
This paper proposes a new method for abnormal behavior detection in surveillance videos via sparse reconstruction analysis. The motion trajectories of objects are firstly defined as fixed-length parametric vectors based on approximating cubic B-spline curves. Then the vectors are classified as behavior patterns and finally distinguished between normal and abnormal behaviors based on sparse reconstruction analysis, in which a classifier is constructed with sparse linear reconstruction coefficients by computing L1-norm minimization and sparse reconstruction residuals learning from labeled training samples. Experimental results on public dataset show the effectiveness of the proposed approach.
Year
DOI
Venue
2011
10.1109/ICIG.2011.104
ICIG
Keywords
Field
DocType
vector classification,sparse reconstruction residuals learning,cubic b-spline curve approximation,approximation theory,l1-norm minimization,sparse reconstruction,abnormal behavior detection,behavior pattern,fixed-length parametric vector,trajectory analysis,sparse linear reconstruction coefficient,image reconstruction,sparse reconstruction analysis,image classification,trajectory representation,surveillance videos,motion trajectory,splines (mathematics),cubic b-spline curve,abnormal behavior,fixed-length parametric,vectors,behavior detection,video surveillance,image motion analysis,trajectory,spline,support vector machine,minimization
Iterative reconstruction,Spline (mathematics),Computer vision,Pattern recognition,Computer science,Sparse approximation,Approximation theory,Parametric statistics,Artificial intelligence,Contextual image classification,Classifier (linguistics),Trajectory
Conference
ISBN
Citations 
PageRank 
978-0-7695-4541-7
20
0.70
References 
Authors
10
4
Name
Order
Citations
PageRank
Ce Li1378.03
Zhenjun Han217616.40
Qixiang Ye391364.51
Jianbin Jiao436732.61