In this thesis, we present a vehicle detection and tracking system based on image processing and pattern recognition of single camera image sequences. Both software design and hardware implementation are considered. In the hypothesis generation (HG) step and the hypothesis verification (HV) step, we use the shadow detection technique combined with the proposed constrained vehicle width/distance ratio to eliminate unreasonable hypotheses. Furthermore, we use SVM classifier, a popular machine learning technique, to verify the generated hypothesis more precisely. In the vehicle tracking step, we limit vehicle tracking duration and periodic vehicle detection mechanisms. These tracking methods alleviate our driver-assistant system from executing complex operations of vehicle detection repeatedly and thus increase system performance without sacrificing too much in case of tracking wrong objects. Based on the the profiling of the software execution time, we implement by hardware the most critical part, the preprocessing of intensity conversion and edge detection. The complete software/hardware embedded system is realized in a FPGA prototype board, so that performance of whole system could achieve real-time processing without too much hardware cost.
| Identifer | oai:union.ndltd.org:NSYSU/oai:NSYSU:etd-0910112-140351 |
| Date | 10 September 2012 |
| Creators | Yeh, Kuan-Fu |
| Contributors | Tso-Bing Juang, Pei-Yung Hsiao, Shen-Fu Hsiao, Ming-Chih Chen |
| Publisher | NSYSU |
| Source Sets | NSYSU Electronic Thesis and Dissertation Archive |
| Language | Cholon |
| Detected Language | English |
| Type | text |
| Format | application/pdf |
| Source | http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0910112-140351 |
| Rights | user_define, Copyright information available at source archive |
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