Traffic sign recognition system (TraSRes)

Added Title

Technical manual

Date of Publication


Document Type

Bachelor's Thesis

Degree Name

Bachelor of Science in Computer Science

Subject Categories

Computer Sciences


College of Computer Studies


Computer Science

Thesis Adviser

Joel P. Ilao

Defense Panel Member

Jesus E. Gonzalez
Russel Lloyd C. Lim


To protect passengers and pedestrians, and to increase the possibility of autonomous vehicle navigation, a vehicle may be guided with minimal human intervention using automated vision-based traffic sign recognition. However, existing studies, addressing only specific aspects of the solution, must be improved. Hence, Traffic Sign Recognition System (TraSReS) is a system that detects and recognizes traffic signs from afar while being invariant to lighting condition, perspective distortion, and partial occlusions, thereby not limiting the application to a fully controlled environment only. Edge and colour information are used to detect potential traffic signs. To increase the probability of proper pattern recognition, the perspective distortion of a potential traffic sign is corrected while following the established aspect ratio and the detected symbol is resized afterwards. A comparative analysis on two pattern recognition techniques is performed.

Tests are conducted on each of the detection and recognition processes using both artificial images and real-world images. The success rate of the red colour detection is 27.5591%, and the success rate of border detection is 89.7436%. The success rate of symbol detection is 100%. All the false positive cases encountered in the detection processes are rejected in the succeeding processes, and the overall success rate of the all detection processes as a whole is 100%. In the two pattern recognition methods studied, success rates of 70.4762% and 41.9048% are obtained. The average time for processing an input image is 90.5753 seconds. In the study, Digital Signal Processing is applied to establish a foundation of a highly useful traffic sign recognition system and to explore its applications in computer vision."

Abstract Format



With: Technical manual





Accession Number


Shelf Location

Archives, The Learning Commons, 12F, Henry Sy Sr. Hall

Physical Description

1 volume (various foliations), illustrations, 28 cm.


Signal processing—Digital techniques; Visual communication—Digital techniques; Traffic signs and signals

Embargo Period


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