http://www.cnr.it/ontology/cnr/individuo/prodotto/ID71076
Automatic Video Surveillance Using Statistical Analysis of Temporal Posture Sequences (Articolo in rivista)
- Type
- Label
- Automatic Video Surveillance Using Statistical Analysis of Temporal Posture Sequences (Articolo in rivista) (literal)
- Anno
- 2006-01-01T00:00:00+01:00 (literal)
- Alternative label
Marco Leo, Tiziana DOrazio, Paolo Spagnolo, Arcangelo Distante (2006)
Automatic Video Surveillance Using Statistical Analysis of Temporal Posture Sequences
in Sensor review
(literal)
- Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#autori
- Marco Leo, Tiziana DOrazio, Paolo Spagnolo, Arcangelo Distante (literal)
- Pagina inizio
- Pagina fine
- Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#numeroVolume
- Rivista
- Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#affiliazioni
- Titolo
- Automatic Video Surveillance Using Statistical Analysis of Temporal Posture Sequences (literal)
- Abstract
- Purpose:
The problem of automatic recognition of human activities is among the most important and
challenging open areas of research in Computer Vision due to the wide range of possible
applications such as surveillance, advanced human-computer interactions, monitoring, etc. This
paper presents statistical computer vision approaches to automatically recognize human activities.
Methodology/Approach:
The human activity recognition process is performed in three steps: first of all human blobs are
segmented by motion analysis; then the human body posture is estimated and finally, for each
activity to be recognized, a temporal model of the detected posture series is generated by Discrete
Hidden Markov Models (DHMM).
Findings:
The system was tested on image sequences acquired in a real archaeological site while some people
simulated both legal and illegal actions. Four kinds of activities were automatically classified with a
high percentage of correct detections.
Research limitations/implications
The proposed approach provides efficient solutions to some of the most common problems in the
human activity recognition research field: high detailed image requirement, sequence alignment and
intensive user interaction in the training phase. The main constraint of this framework is that the
posture estimation approach is not completely view independent;
Practical implications:
Time performance tests were very encouraging for the use of the proposed method in real time
surveillance applications;
the Originality/value of paper:
The proposed framework can work with low cost cameras with large view focal lenses; it does not
need any a priori knowledge of the scene and no intensive user interaction bis required in the early
training phase; (literal)
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