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M.Sc. Idil Esen Zulfikar
Room 127
Email: zulfikar@vision.rwth-aachen.de



Publications


Opening up Open World Tracking


Yang Liu*, Idil Esen Zulfikar*, Jonathon Luiten*, Achal Dave*, Deva Ramanan, Bastian Leibe, Aljoša Ošep, Laura Leal-Taixé
IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2022 (Oral)
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Tracking and detecting any object, including ones never-seen-before during model training, is a crucial but elusive capability of autonomous systems. An autonomous agent that is blind to never-seen-before objects poses a safety hazard when operating in the real world and yet this is how almost all current systems work. One of the main obstacles towards advancing tracking any object is that this task is notoriously difficult to evaluate. A benchmark that would allow us to perform an apples-to-apples comparison of existing efforts is a crucial first step towards advancing this important research field. This paper addresses this evaluation deficit and lays out the landscape and evaluation methodology for detecting and tracking both known and unknown objects in the open-world setting. We propose a new benchmark, TAO-OW: Tracking Any Object in an Open World}, analyze existing efforts in multi-object tracking, and construct a baseline for this task while highlighting future challenges. We hope to open a new front in multi-object tracking research that will hopefully bring us a step closer to intelligent systems that can operate safely in the real world.

» Show BibTeX

@inproceedings{liu2022opening,
title={Opening up Open-World Tracking},
author={Liu, Yang and Zulfikar, Idil Esen and Luiten, Jonathon and Dave, Achal and Ramanan, Deva and Leibe, Bastian and O{\v{s}}ep, Aljo{\v{s}}a and Leal-Taix{\'e}, Laura},
journal={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
year={2022}
}





UnOVOST: Unsupervised Offline Video Object Segmentation and Tracking


Jonathon Luiten*, Idil Esen Zulfikar*, Bastian Leibe
WACV 2020
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We address Unsupervised Video Object Segmentation (UVOS), the task of automatically generating accurate pixel masks for salient objects in a video sequence and of tracking these objects consistently through time, without any input about which objects should be tracked. Towards solving this task, we present UnOVOST (Unsupervised Offline Video Object Segmentation and Tracking) as a simple and generic algorithm which is able to track and segment a large variety of objects. This algorithm builds up tracks in a number stages, first grouping segments into short tracklets that are spatio-temporally consistent, before merging these tracklets into long-term consistent object tracks based on their visual similarity. In order to achieve this we introduce a novel tracklet-based Forest Path Cutting data association algorithm which builds up a decision forest of track hypotheses before cutting this forest into paths that form long-term consistent object tracks. When evaluating our approach on the DAVIS 2017 Unsupervised dataset we obtain state-of-the-art performance with a mean J &F score of 67.9% on the val, 58% on the test-dev and 56.4% on the test-challenge benchmarks, obtaining first place in the DAVIS 2019 Unsupervised Video Object Segmentation Challenge. UnOVOST even performs competitively with many semi-supervised video object segmentation algorithms even though it is not given any input as to which objects should be tracked and segmented.

» Show BibTeX

@inproceedings{luiten2020unovost,
title={UnOVOST: Unsupervised Offline Video Object Segmentation and Tracking},
author={Luiten, Jonathon and Zulfikar, Idil Esen and Leibe, Bastian},
booktitle={Proceedings of the IEEE Winter Conference on Applications in Computer Vision},
year={2020}
}





UnOVOST: Unsupervised Offline Video Object Segmentation and Tracking for the 2019 Unsupervised DAVIS Challenge


Idil Esen Zulfikar*, Jonathon Luiten*, Bastian Leibe
The 2019 DAVIS Challenge on Video Object Segmentation - CVPR Workshops
pubimg

We address Unsupervised Video Object Segmentation (UVOS), the task of automatically generating accurate pixelmasks for salient objects in a video sequence and of track-ing these objects consistently through time, without any in-formation about which objects should be tracked. Towardssolving this task, we present UnOVOST (Unsupervised Of-fline Video Object Segmentation and Tracking) as a simpleand generic algorithm which is able to track a large varietyof objects. This algorithm hierarchically builds up tracksin five stages. First, object proposal masks are generatedusing Mask R-CNN. Second, masks are sub-selected andclipped so that they do not overlap in the image domain.Third, tracklets are generated by grouping object propos-als that are strongly temporally consistent with each otherunder optical flow warping. Fourth, tracklets are mergedinto long-term consistent object tracks using their temporalconsistency and an appearance similarity metric calculatedusing an object re-identification network. Finally, the mostsalient object tracks are selected based on temporal tracklength and detection confidence scores. We evaluate ourapproach on the DAVIS 2017 Unsupervised dataset and ob-tain state-of-the-art performance with a meanJ&Fscoreof 58% on the test-dev benchmark. Our approach furtherachieves first place in the DAVIS 2019 Unsupervised VideoObject Segmentation Challenge with a mean ofJ&Fscoreof 56.4% on the test-challenge benchmark.

» Show BibTeX

@article{ZulfikarLuitenUnOVOST,
title={UnOVOST: Unsupervised Offline Video Object Segmentation and Tracking for the 2019 Unsupervised DAVIS Challenge},
author={Zulfikar, Idil Esen and Luiten, Jonathon and Leibe, Bastian}
booktitle = {The 2019 DAVIS Challenge on Video Object Segmentation - CVPR Workshops},
year = {2019}
}





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