A Novel Method of Pedestrian Detection Aided by Color Self-similarity Feature
| Autor: | Ai-ying Guo, Meihua Xu, Shen Dongyang |
|---|---|
| Rok vydání: | 2016 |
| Předmět: |
Self-similarity
business.industry Computer science Pedestrian detection Detector ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION 020207 software engineering 02 engineering and technology HSL and HSV Feature (computer vision) Histogram Adaboost classifier 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Computer vision Artificial intelligence AdaBoost business |
| Zdroj: | Theory, Methodology, Tools and Applications for Modeling and Simulation of Complex Systems ISBN: 9789811026713 |
| DOI: | 10.1007/978-981-10-2672-0_3 |
| Popis: | Pedestrian detection has been widely applied in intelligent surveillance and driver assistant systems. The histogram of the oriented gradient (HOG) is the most commonly used feature in pedestrian detection algorithms, which is computationally intensive and results in slow detection speed. This paper proposes a method of pedestrian detection, which is based on color self-similarity (CSS) feature and AdaBoost classifier. The color self-similarity (CSS) feature calculates the ratio of two rectangles to measure the self-similarity in HSV color space, and then the AdaBoost classifier is used to screen out the detection windows containing pedestrian. Tests show that this method has the same detection accuracy and faster detection speed compared with HOG detectors. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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