"Shape activity" : a continuous-state HMM for moving/deforming shapes with application to abnormal activity detection

The aim is to model "activity" performed by a group of moving and interacting objects (which can be people, cars, or different rigid components of the human body) and use the models for abnormal activity detection. Previous approaches to modeling group activity include co-occurrence statis...

Ausführliche Beschreibung

Bibliographische Detailangaben
Veröffentlicht in:IEEE transactions on image processing : a publication of the IEEE Signal Processing Society. - 1992. - 14(2005), 10 vom: 01. Okt., Seite 1603-16
1. Verfasser: Vaswani, Namrata (VerfasserIn)
Weitere Verfasser: Roy-Chowdhury, Amit K, Chellappa, Rama
Format: Aufsatz
Sprache:English
Veröffentlicht: 2005
Zugriff auf das übergeordnete Werk:IEEE transactions on image processing : a publication of the IEEE Signal Processing Society
Schlagworte:Evaluation Study Journal Article Research Support, U.S. Gov't, Non-P.H.S.
LEADER 01000naa a22002652 4500
001 NLM158473310
003 DE-627
005 20231223082249.0
007 tu
008 231223s2005 xx ||||| 00| ||eng c
028 5 2 |a pubmed24n0528.xml 
035 |a (DE-627)NLM158473310 
035 |a (NLM)16238065 
040 |a DE-627  |b ger  |c DE-627  |e rakwb 
041 |a eng 
100 1 |a Vaswani, Namrata  |e verfasserin  |4 aut 
245 1 0 |a "Shape activity"  |b a continuous-state HMM for moving/deforming shapes with application to abnormal activity detection 
264 1 |c 2005 
336 |a Text  |b txt  |2 rdacontent 
337 |a ohne Hilfsmittel zu benutzen  |b n  |2 rdamedia 
338 |a Band  |b nc  |2 rdacarrier 
500 |a Date Completed 15.11.2005 
500 |a Date Revised 10.12.2019 
500 |a published: Print 
500 |a Citation Status MEDLINE 
520 |a The aim is to model "activity" performed by a group of moving and interacting objects (which can be people, cars, or different rigid components of the human body) and use the models for abnormal activity detection. Previous approaches to modeling group activity include co-occurrence statistics (individual and joint histograms) and dynamic Bayesian networks, neither of which is applicable when the number of interacting objects is large. We treat the objects as point objects (referred to as "landmarks") and propose to model their changing configuration as a moving and deforming "shape" (using Kendall's shape theory for discrete landmarks). A continuous-state hidden Markov model is defined for landmark shape dynamics in an activity. The configuration of landmarks at a given time forms the observation vector, and the corresponding shape and the scaled Euclidean motion parameters form the hidden-state vector. An abnormal activity is then defined as a change in the shape activity model, which could be slow or drastic and whose parameters are unknown. Results are shown on a real abnormal activity-detection problem involving multiple moving objects 
650 4 |a Evaluation Study 
650 4 |a Journal Article 
650 4 |a Research Support, U.S. Gov't, Non-P.H.S. 
700 1 |a Roy-Chowdhury, Amit K  |e verfasserin  |4 aut 
700 1 |a Chellappa, Rama  |e verfasserin  |4 aut 
773 0 8 |i Enthalten in  |t IEEE transactions on image processing : a publication of the IEEE Signal Processing Society  |d 1992  |g 14(2005), 10 vom: 01. Okt., Seite 1603-16  |w (DE-627)NLM09821456X  |x 1941-0042  |7 nnns 
773 1 8 |g volume:14  |g year:2005  |g number:10  |g day:01  |g month:10  |g pages:1603-16 
912 |a GBV_USEFLAG_A 
912 |a SYSFLAG_A 
912 |a GBV_NLM 
912 |a GBV_ILN_350 
951 |a AR 
952 |d 14  |j 2005  |e 10  |b 01  |c 10  |h 1603-16