Open document
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General information |
| Author |
Zhang, Geng; Yuan, Zejian; Zheng, Nanning |
| Published |
InTech Open Access Publisher, 2013
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| Abstract |
In this paper, we propose an online key object
discovery and tracking system based on visual saliency.
We formulate the problem as a temporally consistent
binary labelling task on a conditional random field and
solve it by using a particle filter. We also propose a
context‐aware saliency measurement, which can be used
to improve the accuracy of any static or dynamic saliency
maps. Our refined saliency maps provide clearer
indications as to where the key object lies. Based on good
saliency cues, we can further segment the key object
inside the resulting bounding box, considering the spatial
and temporal context. We tested our system extensively
on different video clips. The results show that our
method has significantly improved the saliency maps and
tracks the key object accurately. |
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International Journal of Advanced Robotic Systems
Author: 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, [...]
Published: 2004
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