CMRT09: Object Extraction for 3D City Models, Road Databases and Traffic Monitoring - Concepts, Algorithms, and Evaluation
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Table 3. Comparison of results for hyperbolas fits achieved
by Coboid and KNN-Classifier
Figure 7 shows a clear separation of the feature space. A high
classification rate is achieved by both elementary classifiers
(see table 3).
5. CONCLUSION AND OUTLOOK
Table 3 affirms a high reliability on these elementary func
tions, with respect to the used basic classification methods.
Mistakes within the classification mostly reside due to scene
behaviour that occurs fairly rare (e.g. car turning at the inter
section) or is not modelled by the underlying functions (e.g.
pedestrians or cyclists crossing in very custom patterns). The
shown approaches have been tested and verified in a real
time environment with a multi-camera system.
The system shall to automatically observe the traffic on
crossroads in future. For example source-destination depend
ences can be determined with that.
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