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

arXiv: https://arxiv.org/abs/1608.03066 · HTML: https://mlsanity.com/paper/1608.03066/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Aug 2016.

## Built on by (most cited first, 6 of 6)

- [BEV-MODNet: Monocular Camera based Bird's Eye View Moving Object Detection for Autonomous Driving](https://mlsanity.com/paper/2107.04937/) (2021)
- [Surround-View Cameras based Holistic Visual Perception for Automated Driving](https://mlsanity.com/paper/2206.05542/) (2022)
- [Simpler Does It: Generating Semantic Labels with Objectness Guidance](https://mlsanity.com/paper/2110.10335/) (2021)
- [VM-MODNet: Vehicle Motion aware Moving Object Detection for Autonomous Driving](https://mlsanity.com/paper/2104.10985/) (2021)
- [Non-parametric Contextual Relationship Learning for Semantic Video Object Segmentation](https://mlsanity.com/paper/2407.05916/) (2024)
- [Context Propagation from Proposals for Semantic Video Object Segmentation](https://mlsanity.com/paper/2407.06247/) (2024)

## Abstract (arXiv)

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.