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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Jinshi Cui Hongbin Zha Huijing Zhao Shibasaki, R. |
| Copyright Year | 2006 |
| Description | Author affiliation: Nat. Lab. on Machine Perception, Peking Univ., Beijing (Jinshi Cui; Hongbin Zha) |
| Abstract | Laser based people tracking systems have been developed for mobile robotics and intelligent surveillance areas. Existing systems rely on simple laser point clustering methods to extract object locations. However, when dealing with multiple interacting people, laser points of different persons are often interlaced and undistinguishable due to measurement noise and they can not provide reliable features. It causes current systems quite fragile and unreliable. In this paper, we try to explore potentials from multi-level observations including weakly detected features, stably extracted features and foreground points. For inference, detection incorporated joint particle filter is used. And stably extracted features are utilized to properly estimate parameters of dynamic model for each target. In real experiments, we obtain raw data from multiple registered laser scanners, which measure two legs for each people. Evaluations with real data show that the proposed method is more robust and effective than existing approaches |
| Starting Page | 1799 |
| Ending Page | 1804 |
| File Size | 7213292 |
| Page Count | 6 |
| File Format | |
| ISBN | 1424402581 |
| DOI | 10.1109/IROS.2006.282221 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2006-10-09 |
| Publisher Place | China |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Laser noise Feature extraction Intelligent robots Data mining Mobile robots Surveillance Clustering methods Noise measurement Computer vision Particle filters Detection incorporated particle filter Innteracting people tracking Laser scanner Multi-level observations |
| Content Type | Text |
| Resource Type | Article |
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