Generic Motion Based Object Segmentation for Assisted Navigation

Hannuna Sion, Xie Xianghua, Majid Mirmehdi, Neill Campbell

Research output: Chapter in Book/Report/Conference proceedingConference Contribution (Conference Proceeding)

1 Citation (Scopus)


We propose a robust approach to annotating independently moving objects captured by head mounted stereo cameras that are worn by an ambulatory (and visually impaired) user. Initially, sparse optical flow is extracted from a single image stream, in tandem with dense depth maps. Then, using the assumption that apparent movement generated by camera egomotion is dominant, flow corresponding to independently moving objects (IMOs) is robustly segmented using MLESAC. Next, the mode depth of the feature points defining this flow (the foreground) are obtained by aligning them with the depth maps. Finally, a bounding box is scaled proportionally to this mode depth and robustly fit to the foreground points such that the number of inliers is maximised.
Translated title of the contributionGeneric Motion Based Object Segmentation for Assisted Navigation
Original languageEnglish
Title of host publicationVISAPP09
Publication statusPublished - 2009

Bibliographical note

Other page information: -
Conference Proceedings/Title of Journal: VISAPP09
Other identifier: 2000964


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