Year: 2016
Type: conference-paper
Abstract: Motions are important features for robot vision as we live in a dynamic world. Detecting moving objects is crucial\nfor mobile robots and computer vision systems. This paper investigates an architecture for the segmentation of\nmoving objects from image sequences. Objects are represented as groups of SIFT feature points. Instead of\ntracking the feature points over a sequence of frames, the movements of feature points between two successive\nframes are used. The segmentation of motions of each pair of frames is based on the expectation-maximization\nalgorithm. The segmentation algorithm is iteratively applied over all frames of the sequence and the results are\ncombined using Bayesian update.
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W2797063365