# Robust Multi-body Feature Tracker: A Segmentation-free Approach

arXiv: https://arxiv.org/abs/1603.00110 · HTML: https://mlsanity.com/paper/1603.00110/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Mar 2016.

## Built on by (most cited first, 2 of 2)

- [Self-Supervised Learning of Depth and Ego-Motion from Video by Alternative Training and Geometric Constraints from 3D to 2D](https://mlsanity.com/paper/2108.01980/) (2021)
- [Deep Feature Tracker: A Novel Application for Deep Convolutional Neural Networks](https://mlsanity.com/paper/2108.00105/) (2021)

## Abstract (arXiv)

Feature tracking is a fundamental problem in computer vision, with applications in many computer vision tasks, such as visual SLAM and action recognition. This paper introduces a novel multi-body feature tracker that exploits a multi-body rigidity assumption to improve tracking robustness under a general perspective camera model. A conventional approach to addressing this problem would consist of alternating between solving two subtasks: motion segmentation and feature tracking under rigidity constraints for each segment. This approach, however, requires knowing the number of motions, as well as assigning points to motion groups, which is typically sensitive to the motion estimates. By contrast, here, we introduce a segmentation-free solution to multi-body feature tracking that bypasses the motion assignment step and reduces to solving a series of subproblems with closed-form solutions. Our experiments demonstrate the benefits of our approach in terms of tracking accuracy and robustness to noise.