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| Content Provider | ACM Digital Library |
|---|---|
| Author | Marginean, Marcel-Titus Lu, Chao |
| Abstract | Tracking multiple persons/robots/pets and moving objects is an essential task for situation awareness in robot navigation and operation. It is also a relatively complicated problem of computer vision and multiple solutions have been proposed in literature. In this paper we are exploring a novel method of object tracking using computer vision by fusing multiple techniques into a single tracker implementation. The main goal of this method is to perform high confidence data associations as soon as possible in order to be able to provide tracking information to a moving robot in real time with attempts to minimize the CPU utilization for tracking whenever possible since the Base Station computer is being shared with multiple software modules. |
| Starting Page | 88 |
| Ending Page | 94 |
| Page Count | 7 |
| File Format | |
| ISBN | 9781450330602 |
| DOI | 10.1145/2663761.2663762 |
| Language | English |
| Publisher | Association for Computing Machinery (ACM) |
| Publisher Date | 2014-10-05 |
| Publisher Place | New York |
| Access Restriction | Subscribed |
| Subject Keyword | Robot navigation Segmentation Kalman filter Feature matching Object tracking |
| Content Type | Text |
| Resource Type | Article |
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