Video object segmentation is challenging due to fast moving objects, deforming shapes, and cluttered backgrounds. Optical flow can be used to propagate an object segmentation over time but, unfortunately, flow is often inaccurate, particularly around object boundaries. Such boundaries are precisely where we want our segmentation to be accurate. To obtain accurate segmentation across time, we propose an efficient algorithm that considers video segmentation and optical flow estimation simultaneously. For video segmentation, we formulate a principled, multiscale, spatio-temporal objective function that uses optical flow to propagate information between frames. For optical flow estimation, particularly at object boundaries, we compute the flow independently in the segmented regions and recompose the results. We call the process object flow and demonstrate the effectiveness of jointly optimizing optical flow and video segmentation using an iterative scheme. Experiments on the SegTrack v2 and Youtube-Objects datasets show that the proposed algorithm performs favorably against the other state-of-the-art methods.
|Title of host publication||Proceedings - 29th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2016|
|Publisher||IEEE Computer Society|
|Number of pages||10|
|Publication status||Published - 2016 Dec 9|
|Event||29th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2016 - Las Vegas, United States|
Duration: 2016 Jun 26 → 2016 Jul 1
|Name||Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition|
|Conference||29th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2016|
|Period||16/6/26 → 16/7/1|
Bibliographical notePublisher Copyright:
© 2016 IEEE.
All Science Journal Classification (ASJC) codes
- Computer Vision and Pattern Recognition