Mixed-State Models for Nonstationary Multiobject Activities
We present a mixed-state space approach for modeling and segmenting human activities. The discrete-valued component of the mixed state represents higher-level behavior while the continuous state models the dynamics within behavioral segments. A basis of behaviors based on generic properties of motio...
| Main Authors: | , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
SpringerOpen
2007-01-01
|
| Series: | EURASIP Journal on Advances in Signal Processing |
| Online Access: | http://dx.doi.org/10.1155/2007/65989 |
| _version_ | 1818522175014961152 |
|---|---|
| author | Rama Chellappa Naresh P. Cuntoor |
| author_facet | Rama Chellappa Naresh P. Cuntoor |
| author_sort | Rama Chellappa |
| collection | DOAJ |
| description | We present a mixed-state space approach for modeling and segmenting human activities. The discrete-valued component of the mixed state represents higher-level behavior while the continuous state models the dynamics within behavioral segments. A basis of behaviors based on generic properties of motion trajectories is chosen to characterize segments of activities. A Viterbi-based algorithm to detect boundaries between segments is described. The usefulness of the proposed approach for temporal segmentation and anomaly detection is illustrated using the TSA airport tarmac surveillance dataset, the bank monitoring dataset, and the UCF database of human actions. |
| first_indexed | 2024-12-11T05:29:49Z |
| format | Article |
| id | doaj.art-b9c640eb5cd04140a4823f36b705a763 |
| institution | Directory Open Access Journal |
| issn | 1687-6172 1687-6180 |
| language | English |
| last_indexed | 2024-12-11T05:29:49Z |
| publishDate | 2007-01-01 |
| publisher | SpringerOpen |
| record_format | Article |
| series | EURASIP Journal on Advances in Signal Processing |
| spelling | doaj.art-b9c640eb5cd04140a4823f36b705a7632022-12-22T01:19:28ZengSpringerOpenEURASIP Journal on Advances in Signal Processing1687-61721687-61802007-01-01200710.1155/2007/65989Mixed-State Models for Nonstationary Multiobject ActivitiesRama ChellappaNaresh P. CuntoorWe present a mixed-state space approach for modeling and segmenting human activities. The discrete-valued component of the mixed state represents higher-level behavior while the continuous state models the dynamics within behavioral segments. A basis of behaviors based on generic properties of motion trajectories is chosen to characterize segments of activities. A Viterbi-based algorithm to detect boundaries between segments is described. The usefulness of the proposed approach for temporal segmentation and anomaly detection is illustrated using the TSA airport tarmac surveillance dataset, the bank monitoring dataset, and the UCF database of human actions.http://dx.doi.org/10.1155/2007/65989 |
| spellingShingle | Rama Chellappa Naresh P. Cuntoor Mixed-State Models for Nonstationary Multiobject Activities EURASIP Journal on Advances in Signal Processing |
| title | Mixed-State Models for Nonstationary Multiobject Activities |
| title_full | Mixed-State Models for Nonstationary Multiobject Activities |
| title_fullStr | Mixed-State Models for Nonstationary Multiobject Activities |
| title_full_unstemmed | Mixed-State Models for Nonstationary Multiobject Activities |
| title_short | Mixed-State Models for Nonstationary Multiobject Activities |
| title_sort | mixed state models for nonstationary multiobject activities |
| url | http://dx.doi.org/10.1155/2007/65989 |
| work_keys_str_mv | AT ramachellappa mixedstatemodelsfornonstationarymultiobjectactivities AT nareshpcuntoor mixedstatemodelsfornonstationarymultiobjectactivities |