Research Article
Camera Localization in Distributed Networks Using Trajectory Estimation
Table 1
Summary of the state-of-the-art approaches for sensor localization.
| Classes | Approaches | References |
| Fine-grained localization | Maximum likelihood, trigonometric, | [13, 14, 30], | Received signal strength, time difference of arrival | [9, 11] |
| Coarse-grained (nonstatistical localization) | Multidimensional scaling | [16ā19] | Moving scene features | [20] | Active badge location | [12] |
| Coarse-grained (statistical localization) | Linear regression and Kalman filter | [31] | Maximum a posterior probability estimation | [21, 27, 32] | Simultaneous localization and mapping | [25, 26] | Structure from motion | [22ā24] | Tracking and camera field of view information | [28] | Vanishing points and known position | [29] |
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