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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Daubney, B. Gibson, D. Campbell, N. |
| Copyright Year | 2008 |
| Description | Author affiliation: Dept. of Comput. Sci., Univ. of Bristol, Bristol (Daubney, B.; Gibson, D.; Campbell, N.) |
| Abstract | We present a method that is capable of tracking and estimating pose of articulated objects in real-time. This is achieved by using a bottom-up approach to detect instances of the object in each frame, these detections are then linked together using a high-level a priori motion model. Unlike other approaches that rely on appearance, our method is entirely dependent on motion; initial low-level part detection is based on how a region moves as opposed to its appearance. This work is best described as pictorial structures using motion. A sparse cloud of points extracted using a standard feature tracker are used as observational data, this data contains noise that is not Gaussian in nature but systematic due to tracking errors. Using a probabilistic framework we are able to overcome both corrupt and missing data whilst still inferring new poses from a generative model. Our approach requires no manual initialisation and we show results for a number of complex scenes and different classes of articulated object, this demonstrates both the robustness and versatility of the presented technique. |
| Starting Page | 1 |
| Ending Page | 8 |
| File Size | 1769437 |
| Page Count | 8 |
| File Format | |
| ISBN | 9781424422425 |
| ISSN | 10636919 |
| DOI | 10.1109/CVPR.2008.4587530 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2008-06-23 |
| Publisher Place | USA |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Motion estimation Motion detection Object detection Data mining Tracking Dynamic programming Belief propagation Computer science Clouds Gaussian noise |
| Content Type | Text |
| Resource Type | Article |
| Subject | Computer Vision and Pattern Recognition Software |
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