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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | YoungJoon Yoo Hawook Jeong Soo Wan Kim Jin Young Choi |
| Copyright Year | 2014 |
| Description | Author affiliation: Dept. of Electr. & Comput. Eng., Seoul Nat. Univ., Seoul, South Korea (YoungJoon Yoo; Hawook Jeong; Soo Wan Kim; Jin Young Choi) |
| Abstract | This paper proposes a transfer learning scheme for traffic pattern analysis where the transferred classifier could be trained with a small number of samples. First we make feature descriptors to represent the traffic trajectories so that they should be adequate to transfer and classify the traffic patterns. Then, we use support vector machine (SVM) to learn the feature descriptors of traffic trajectories. The transfer learning scheme is formulated by a convex optimization problem using the geometric relation between target and source patterns. Not only parameters of SVM but also the geometric relation are found at the same time through two step minimization process of the optimization problem. Through experiments on various surveillance videos, the proposed formulation is shown to be valid by investigating the improvement of performance compared to a transfer scheme without the proposed geometric relation as well as SVM without transfer scheme. |
| Starting Page | 4158 |
| Ending Page | 4163 |
| File Size | 762983 |
| Page Count | 6 |
| File Format | |
| ISBN | 9781479952090 |
| ISSN | 10514651 |
| DOI | 10.1109/ICPR.2014.713 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2014-08-24 |
| Publisher Place | Sweden |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Trajectory Vectors Support vector machines Videos Mathematical model Linear programming Surveillance |
| Content Type | Text |
| Resource Type | Article |
| Subject | Computer Vision and Pattern Recognition |
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