JOTS: Joint Online Tracking and Segmentation

Longyin Wen, Dawei Du, Zhen Lei, Stan Z. Li, Ming-Hsuan Yang; The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2015, pp. 2226-2234

Abstract


We present a novel Joint Online Tracking and Segmentation (JOTS) algorithm which integrates the multi-part tracking and segmentation into a unified energy optimization framework to handle the video segmentation task. The multi-part segmentation is posed as a pixel-level label assignment task with regularization according to the estimated part models, and tracking is formulated as estimating the part models based on the pixel labels, which in turn is used to refine the model. The multi-part tracking and segmentation are carried out iteratively to minimize the proposed objective function by a RANSAC-style approach. Extensive experiments on the SegTrack and SegTrack v2 databases demonstrate that the proposed algorithm performs favorably against the state-of-the-art methods.

Related Material


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[bibtex]
@InProceedings{Wen_2015_CVPR,
author = {Wen, Longyin and Du, Dawei and Lei, Zhen and Li, Stan Z. and Yang, Ming-Hsuan},
title = {JOTS: Joint Online Tracking and Segmentation},
booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2015}
}