TY - CONF
T1 - Statistical shape theory for activity modeling
T2 - Acoustics, Speech, and Signal Processing, 2003. Proceedings. (ICASSP '03). 2003 IEEE International Conference on
Y1 - 2003
A1 - Vaswani, N.
A1 - Chowdhury, A.R.
A1 - Chellapa, Rama
KW - abnormal
KW - activities
KW - activity
KW - analysis;
KW - behavior;
KW - classification;
KW - data;
KW - image
KW - mass;
KW - matching;
KW - modeling;
KW - monitoring;
KW - moving
KW - normal
KW - particle;
KW - pattern
KW - pattern;
KW - point
KW - polygonal
KW - probability;
KW - problem;
KW - processing;
KW - sequence;
KW - sequences;
KW - SHAPE
KW - shape;
KW - signal
KW - statistical
KW - Surveillance
KW - surveillance;
KW - theory;
KW - video
AB - Monitoring activities in a certain region from video data is an important surveillance problem. The goal is to learn the pattern of normal activities and detect unusual ones by identifying activities that deviate appreciably from the typical ones. We propose an approach using statistical shape theory based on the shape model of D.G. Kendall et al. (see "Shape and Shape Theory", John Wiley and Sons, 1999). In a low resolution video, each moving object is best represented as a moving point mass or particle. In this case, an activity can be defined by the interactions of all or some of these moving particles over time. We model this configuration of the particles by a polygonal shape formed from the locations of the points in a frame and the activity by the deformation of the polygons in time. These parameters are learned for each typical activity. Given a test video sequence, an activity is classified as abnormal if the probability for the sequence (represented by the mean shape and the dynamics of the deviations), given the model, is below a certain threshold The approach gives very encouraging results in surveillance applications using a single camera and is able to identify various kinds of abnormal behavior.
JA - Acoustics, Speech, and Signal Processing, 2003. Proceedings. (ICASSP '03). 2003 IEEE International Conference on
VL - 3
M3 - 10.1109/ICASSP.2003.1199519
ER -