# Object Detection, Tracking, and Motion Segmentation for Object-level Video Segmentation

Source: [Object Detection, Tracking, and Motion Segmentation for Object-level Video Segmentation](<https://www.sota2.com/research/paper/68fa62a21e209131ce9fa677>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-25T20:23:26.300Z

Authors: Benjamin Drayer, Thomas Brox

Published: 2016-08-10T07:46:56.000Z

## Abstract

We present an approach for object segmentation in videos that combines frame-level object detection with concepts from object tracking and motion segmentation. The approach extracts temporally consistent object tubes based on an off-the-shelf detector. Besides the class label for each tube, this provides a location prior that is independent of motion. For the final video segmentation, we combine this information with motion cues. The method overcomes the typical problems of weakly supervised/unsupervised video segmentation, such as scenes with no motion, dominant camera motion, and objects that move as a unit. In contrast to most tracking methods, it provides an accurate, temporally consistent segmentation of each object. We report results on four video segmentation datasets: YouTube Objects, SegTrackv2, egoMotion, and FBMS.

## Recorded benchmark results

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## Original sources and implementation

[Original paper](<https://arxiv.org/abs/1608.03066>)

[PDF](<https://arxiv.org/pdf/1608.03066.pdf>)