Publications

Year: Author:

Aljoša Ošep, Wolfgang Mehner, Paul Voigtlaender, Bastian Leibe
Accepted for IEEE Int. Conference on Robotics and Automation (ICRA'18), to appear

The most common paradigm for vision-based multi-object tracking is tracking-by-detection, due to the availability of reliable detectors for several important object categories such as cars and pedestrians. However, future mobile systems will need a capability to cope with rich human-made environments, in which obtaining detectors for every possible object category would be infeasible. In this paper, we propose a model-free multi-object tracking approach that uses a category-agnostic image segmentation method to track objects. We present an efficient segmentation mask-based tracker which associates pixel-precise masks reported by the segmentation. Our approach can utilize semantic information whenever it is available for classifying objects at the track level, while retaining the capability to track generic unknown objects in the absence of such information. We demonstrate experimentally that our approach achieves performance comparable to state-of-the-art tracking-by-detection methods for popular object categories such as cars and pedestrians. Additionally, we show that the proposed method can discover and robustly track a large variety of other objects.

» Show BibTeX

@article{Osep18ICRA,
author = {O\v{s}ep, Aljo\v{s}a and Mehner, Wolfgang and Voigtlaender, Paul and Leibe, Bastian},
title = {Track, then Decide: Category-Agnostic Vision-based Multi-Object Tracking},
journal = {ICRA},
year = {2018}
}






Aljoša Ošep, Paul Voigtlaender, Jonathon Luiten, Stefan Breuers, Bastian Leibe
arXiv:1712.08832

We explore object discovery and detector adaptation based on unlabeled video sequences captured from a mobile platform. We propose a fully automatic approach for object mining from video which builds upon a generic object tracking approach. By applying this method to three large video datasets from autonomous driving and mobile robotics scenarios, we demonstrate its robustness and generality. Based on the object mining results, we propose a novel approach for unsupervised object discovery by appearance-based clustering. We show that this approach successfully discovers interesting objects relevant to driving scenarios. In addition, we perform self-supervised detector adaptation in order to improve detection performance on the KITTI dataset for existing categories. Our approach has direct relevance for enabling large-scale object learning for autonomous driving.

» Show BibTeX

@article{OsepVoigtlaender18arxiv,
title={Large-Scale Object Discovery and Detector Adaptation from Unlabeled Video},
author={Aljo\v{s}a O\v{s}ep and Paul Voigtlaender and Jonathon Luiten and Stefan Breuers and Bastian Leibe},
journal={arXiv preprint arXiv:1712.08832},
year={2018}
}






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