Based on .Net Framework4.0 development platform and Visual C# language, this thesis presents various methods of performing lane detection and preceding vehicle detection/tracking with code optimization and acceleration to reduce the execution time. The thesis consists of two major parts: vehicle detection and tracking. In the part of detection, driving lanes are identified first and then the preceding vehicles between the left lane and right lane are detected using the shadow information beneath vehicles. In vehicle tracking, three-pass search method is used to find the matched vehicles based on the detection results in the previous frames. According to our experiments, the preprocessing (including color-intensity conversion) takes a significant portion of total execution time. We propose different methods to optimize the code and speed up the software execution using pure C # pointers, OPENCV, and OPENCL etc. Experimental results show that the fastest detection/tracking speed can reach more than 30 frames per second (fps) using PC with i7-2600 3.4Ghz CPU. Except for OPENCV with execution rate of 18 fps, the rest of methods have up to 28 fps processing rate of almost the real-time speed. We also add the auxiliary vehicle information, such as preceding vehicle distance and vehicle offset warning.
| Identifer | oai:union.ndltd.org:NSYSU/oai:NSYSU:etd-0102113-121204 |
| Date | 02 January 2013 |
| Creators | Chen, Jie-Qi |
| Contributors | Chung-Nan Lee, 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-0102113-121204 |
| Rights | user_define, Copyright information available at source archive |
Page generated in 0.0021 seconds