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Informations générales |
| Auteur |
Kokkinos, Michalis; Doulamis, Nikolaos D.; Doulamis, Anastasios D. |
| Publié |
InTech Open Access Publisher, 2013
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| Abstract |
Detecting a fall through visual cues is
emerging as a hot research agenda for improving the
independence of the elderly. However, the traditional
motion‐based algorithms are very sensitive to noise,
reducing fall detection accuracy. Another approach is to
efficiently localize and then track the foreground object
followed by measurements that aid the identification of
a fall. However, to perform robust and stable tracking
over a long time is a challenging research aspect in
computer vision society. In this paper, we introduce a
stable human tracker able to efficiently cope with the
trade‐off between model stability (accurate tracking
performance) and adaptability (model evolution to
visual changes). In particular, we introduce local
geometrically enriched mixture models for background
modelling. Then, we incorporate iterative motion
information methods, constrained by shape and time
properties, to estimate high confidence image regions
for background model updating. This way, we are able
to detect and track the foreground objects even when
visual conditions are dynamically changed over time
(luminosity or background/foreground changes or
active cameras). |
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International Journal of Advanced Robotic Systems
Auteur: Ottaviano, Erika; Ceccarelli, Marco; Husty, Manfred; Yu, Sung-Hoon; Kim, Yong-Tae; Park, Chang-Woo; Hyun, Chang-Ho; Chen, Xiulong; Feng, Weiming; Sun, Xianyang; Gao, Qing; Grigorescu, Sorin M.; Pozna, Claudiu; Liu, Wanli; Zhankui, Wang; Guo, Meng; Fu, Guoyu; Zhang, Jin; Chen, Wenyuan; Peng, Fengchao; Yang, Pei; Chen, Chunlin; Ding, Rui; Yu, Junzhi; Yang, Qinghai; Tan, Min; Polden, Joseph; Pan, [...]
Publié: 2004
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