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Informations générales |
| Auteur |
Kim, Jaemyun; Murshed, Mahbub; Rivera, Adin Ramirez; Chae, Oksam |
| Publié |
InTech Open Access Publisher, 2013
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| Abstract |
We propose an edge‐segment‐based statistical
background modelling algorithm to detect the moving
edges for the detection of moving objects using a static
camera. Traditional pixel intensity‐based background
modelling algorithms face difficulties in dynamic
environments since they cannot handle sudden changes
in illumination. They also bring out ghosts when a
sudden change occurs in the scene. To cope with this
issue, intensity and noise robust edge‐based features have
emerged. However, existing edge‐pixel‐based methods
suffer from scattered moving edge pixels since they
cannot utilize the shape. Moreover, traditional segmentbased
methods cannot handle edge shape variations and
miss moving edges when they come close to the
background edges. Unlike traditional approaches, our
proposed method builds the background model from
ordinary training frames that may contain moving
objects. Furthermore, it does not leave any ghosts behind.
Moreover, our method uses an automatic threshold for
every background edge distribution for matching. This
makes our approach robust to illumination change,
camera movement and background motion. Experiments
show that our method outperforms others and can detect
moving edges efficiently despite the above mentioned
difficulties. |
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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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