Active tracking with accelerated image processing in hardware
| dc.contributor.advisor | Kent, Kenneth | |
| dc.contributor.advisor | Herpers, Rainer | |
| dc.contributor.author | Bochem, Alexander | |
| dc.date.accessioned | 2023-03-01T16:22:31Z | |
| dc.date.available | 2023-03-01T16:22:31Z | |
| dc.date.issued | 2010 | |
| dc.date.updated | 2016-12-13T00:00:00Z | |
| dc.description.abstract | This thesis work presents the implementation and validation of image processing problems in hardware to estimate the performance and precision gain. It compares the implementation for the addressed problem on a Field Programmable Gate Array (FPGA) with a software implementation for a General Purpose Processor (GPP) architecture. For both solutions the implementation costs for their development is an important aspect in the validation. The analysis of the exibility and extendability that can be achieved by a modular implementation for the FPGA design was another major aspect. One addressed problem of this work is the tracking of the detected BLOBs in continuous image material. This has been implemented for the FPGA platform and the GPP architecture. Both approaches have been compared with respect to performance and precision. This research project is motivated by the MI6 project of the Computer Vision research group, which is located at the Bonn-Rhein-Sieg University of Applied Sciences. The intent of the MI6 project is the tracking of a user in an immersive environment. The proposed solution is to attach a light emitting device to the user for tracking the emitted light dots on the projection surface of the immersive environment. Having the center points of those light dots would allow the estimation of the user's position and orientation. One major issue that makes Computer Vision problems computationally expensive is the high amount of data that has to be processed in real-time. Therefore, one major target for the implementation was to get a processing speed of more than 30 frames per second. This would allow the system to realize feedback to the user in a response time which is faster than the human visual perception. One problem that comes with the idea of using a light emitting device to represent the user, is the precision error. Dependent on the resolution of the tracked projection surface of the immersive environment, a pixel might be several cm2 in size. Having a precision error of only a few pixels, might lead to an offset in the estimated user's position of several cm. In this research work the development and validation of a detection and tracking system for BLOBs on a Cyclone II FPGA from Altera has been implemented. The system supports different input devices for the image acquisition and can perform detection and tracking for five to eight BLOBs. A further extension of the design with other input devices or to support the detection is possible with some constraints, which comes with the available resources on the target platform. Additional modules for compressing the image data based on run-length encoding and sub-pixel precision for the computed BLOB center-points have been designed. For the comparison of the FPGA approach for BLOB tracking a similar implementation in software using a multi-threaded approach has been realized. The system can transmit the detection or tracking results on two available communication interfaces, USB and RS232. The analysis of the hardware solution showed a similar precision for the BLOB detection and tracking as the software approach. One problem is the large increase of the allocated resources when extending the system to process more BLOBs. With one of the target platforms, the DE2-70 board from Altera, the BLOB detection could be extended to process up to thirty BLOBs. The implementation of the tracking approach in hardware required much more effort than the software solution. The design of high level problems in hardware for this case are more expensive than the software implementation. The search and match steps in the tracking approach could be realized more efficiently and reliably in software. The additional pre-processing modules for sub-pixel precision and run-length-encoding helped to increase the system's performance and precision. | |
| dc.description.copyright | © Alexander Bochem, 2010 | |
| dc.description.note | Degree name on title page is mislabeled as "Master of Science In the Graduate Academic Unit of Computer Science" Changed to "Master of Computer Science.." Electronic Only. (UNB thesis number) Thesis 8658 (OCoLC) 960872346 | |
| dc.description.note | M.C.S., University of New Brunswick, Faculty of Computer Science, 2010. | |
| dc.format | text/xml | |
| dc.format.extent | vii, 98 pages | |
| dc.format.medium | electronic | |
| dc.identifier.oclc | (OCoLC) 960872346 | |
| dc.identifier.other | Thesis 8658 | |
| dc.identifier.uri | https://unbscholar.lib.unb.ca/handle/1882/13678 | |
| dc.language.iso | en_CA | |
| dc.publisher | University of New Brunswick | |
| dc.rights | http://purl.org/coar/access_right/c_abf2 | |
| dc.subject.discipline | Computer Science | |
| dc.subject.lcsh | Computer input-output equipment. | |
| dc.subject.lcsh | Virtual reality. | |
| dc.subject.lcsh | Field programmable gate arrays. | |
| dc.subject.lcsh | Computer vision. | |
| dc.subject.lcsh | Image processing. | |
| dc.title | Active tracking with accelerated image processing in hardware | |
| dc.type | master thesis | |
| thesis.degree.discipline | Computer Science | |
| thesis.degree.fullname | Master of Computer Science | |
| thesis.degree.grantor | University of New Brunswick | |
| thesis.degree.level | masters | |
| thesis.degree.name | M.C.S. |
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