Hierarchical Method for Stereophotogrammetric Multi-object-position Measurement

Tornow, M.; Michaelis, Bernd; Kuhn, R.W.; Calow, Roman; Mecke, Rüdiger

doi:10.1007/978-3-540-45243-0_22

Cited by 4 publications

(1 citation statement)

References 5 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…According to the given timing-requirements a hardware-implementation [6] is the most capable solution for this task. Therefore a hardwareoptimized correlation method for 3-D-measurement [7] has to be developed, thus it can be achieved within 100s. The basic idea is that far away objects are very small (a few pixels) with correspondingly low disparity values, objects at close range are relatively large with high disparity values.…”

Section: Hardware -Depth Mapmentioning

confidence: 99%

Real-time, 3-D multi object position estimation and tracking

Kaszubiak

Tornow

Kuhn

et al. 2004

Proceedings of the 17th International Conference on Pattern Recognition, 2004. ICPR 2004.

Self Cite

View full text Add to dashboard Cite

For autonomously acting robots and driver assistance systems powerful optical stereo sensor systems are required. Object positions and environmental conditions have to be acquired in real-time. In this paper a hardware-software co-design is applied, acting within the presented stereophotogrammetric system. For calculation of the depth map an optimized algorithm is implemented as a hierarchical parallel hardware solution. By adapting the image resolution to the distance, real-time processing is possible. The object clustering and the tracking is realized in a processor. The density distribution of the disparity in the depth map (disparity histogram) is used for object detection. A Kalman filter stabilizes the parameters of the results.

show abstract

Section: Hardware -Depth Mapmentioning

confidence: 99%