Detecting Pedestrians Using Patterns of Motion and Appearance
| Autor: | Michael Jones, Paul A. Viola, Daniel Snow |
|---|---|
| Rok vydání: | 2005 |
| Předmět: |
Implicit Shape Model
Boosting (machine learning) Pixel business.industry Computer science Pedestrian detection Detector ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION Artificial Intelligence Computer vision Computer Vision and Pattern Recognition Artificial intelligence AdaBoost business Software |
| Zdroj: | ICCV |
| ISSN: | 1573-1405 0920-5691 |
| DOI: | 10.1007/s11263-005-6644-8 |
| Popis: | This paper describes a pedestrian detection system that integratesimage intensity information with motion information.We use a detection style algorithm that scans a detectorover two consecutive frames of a video sequence. Thedetector is trained (using AdaBoost) to take advantage ofboth motion and appearance information to detect a walkingperson. Past approaches have built detectors based onmotion information or detectors based on appearance information,but ours is the first to combine both sources ofinformation in a single detector. The implementation describedruns at about 4 frames/second, detects pedestriansat very small scales (as small as 20x15 pixels), and has avery low false positive rate.Our approach builds on the detection work of Viola andJones. Novel contributions of this paper include: i) developmentof a representation of image motion which is extremelyefficient, and ii) implementation of a state of theart pedestrian detection system which operates on low resolutionimages under difficult conditions (such as rain andsnow). |
| Databáze: | OpenAIRE |
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