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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Minmin Shen Szyszka, P. Galizia, C.G. Merhof, D. |
| Copyright Year | 2013 |
| Abstract | Automatic tracking of the movement of bee's antennae and mouthparts is necessary for studying associative learning of individuals. However, the problem of tracking them is challenging: First, the different classes of objects possess similar appearance and are close to each other. Second, tracking gaps are often present, due to the low frame-rate of the acquired video and the fast motion of the objects. Most existing insect tracking approaches have been developed for slow moving objects, and are not suitable for this application. In this paper, a novel Bayesian framework is proposed to automatically track bees' antennae and their mouthparts. This framework incorporates information about their kinematics, shape, order and temporal correlation between neighboring frames. Experimental evaluation demonstrates the effectiveness and efficiency of the proposed framework. |
| Starting Page | 4112 |
| Ending Page | 4116 |
| File Size | 1868183 |
| Page Count | 5 |
| File Format | |
| ISBN | 9781479923410 |
| DOI | 10.1109/ICIP.2013.6738847 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2013-09-15 |
| Publisher Place | Australia |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | merged detections multi-target tracking bee antennae and mandibles and proboscis splitted detections |
| Content Type | Text |
| Resource Type | Article |
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