Motions are important features for robot vision as we live in a dynamic world. Detecting moving objects is crucial for mobile robots and computer vision systems. This paper investigates an architecture for the segmentation of moving objects from image sequences. Objects are represented as groups of SIFT feature points. Instead of tracking the feature points over a sequence of frames, the movements of feature points between two successive frames are used. The segmentation of motions of each pair of frames is based on the expectation-maximization algorithm. The segmentation algorithm is iteratively applied over all frames of the sequence and the results are combined using Bayesian update.
|Title of host publication|| International Conferences in Central Europe on Computer Graphics, Visualization and Computer Vision (WSCG)|
|Editors||Zhigeng Pan, Vaclav Skala|
|Publisher||Vaclav Skala Union Agency|
|Number of pages||9|
|Publication status||Published - 2016|
|Series||Computer Science Research Notes|