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- W2018579935 abstract "The detection of pedestrians in real-world scenes is a daunting task, especially in crowded situations. Our experienceover the last years has shown that active shape models (ASM) can contribute significantly to a robust pedestriandetection system.The paper starts with an overview of shape model approaches, it then explains our approach which builds on top ofEigenshape models which have been trained using real-world data. These models are placed over candidate regions andmatched to image gradients using a scoring function which integrates i) point distribution, ii) local gradient orientationsiii) local image gradient strengths. A matching and shape model update process is iteratively applied in order to fit theflexible models to the local image content.The weights of the scoring function have a significant impact on the ASM performance. We analyze different settings ofscoring weights for gradient magnitude, relative orientation differences, distance between model and gradient in anexperiment which uses real-world data. Although for only one pedestrian model in an image computation time is low, thenumber of necessary processing cycles which is needed to track many people in crowded scenes can become thebottleneck in a real-time application. We describe the measures which have been taken in order to improve the speed ofthe ASM implementation and make it real-time capable." @default.
- W2018579935 created "2016-06-24" @default.
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- W2018579935 date "2009-01-18" @default.
- W2018579935 modified "2023-09-23" @default.
- W2018579935 title "People detection in crowded scenes using active contour models" @default.
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- W2018579935 doi "https://doi.org/10.1117/12.807720" @default.
- W2018579935 hasPublicationYear "2009" @default.
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