Moving vehicles segmentation based on Bayesian framework for Gaussian motion model
Pattern Recognition Letters
Classification of moving humans using eigen-features and support vector machines
CAIP'05 Proceedings of the 11th international conference on Computer Analysis of Images and Patterns
Efficient human detection in crowded environment based on motion and appearance information
Proceedings of the Fifth International Conference on Internet Multimedia Computing and Service
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Object recognition, i. e. classification of objects into one of several known object classes, generally is a difficult task. In this paper we address the problem of detecting and classifying moving objects in image sequences from traffic scenes recorded with a static camera. In the first step, a statistical, illumination invariant motion detection algorithm is used to produce binary masks of the scene-changes. Next, Fourier descriptors of the shapes from the refined masks are computed and used as feature vectors describing the different objects in the scene. Finally, a feed-forward neural net is used to distinguish between humans, vehicles, and background clutters.