The Visual Object Tracking VOT2016 Challenge Results
- M. Kristan, A. Leonardis, Zhizhen Chi
- Computer ScienceECCV Workshops
- 8 October 2016
The Visual Object Tracking challenge VOT2016 goes beyond its predecessors by introducing a new semi-automatic ground truth bounding box annotation methodology and extending the evaluation system with the no-reset experiment.
The Sixth Visual Object Tracking VOT2018 Challenge Results
- M. Kristan, A. Leonardis, Zhiqun He
- Computer ScienceECCV Workshops
- 8 September 2018
The Visual Object Tracking challenge VOT2018 is the sixth annual tracker benchmarking activity organized by the VOT initiative. Results of over eighty trackers are presented; many are…
Robust Object Detection with Interleaved Categorization and Segmentation
- B. Leibe, A. Leonardis, B. Schiele
- Computer ScienceInternational Journal of Computer Vision
- 1 May 2008
A novel method for detecting and localizing objects of a visual category in cluttered real-world scenes that is applicable to a range of different object categories, including both rigid and articulated objects and able to achieve competitive object detection performance from training sets that are between one and two orders of magnitude smaller than those used in comparable systems.
Combined Object Categorization and Segmentation With an Implicit Shape Model
- B. Leibe, A. Leonardis, B. Schiele
- Computer Science
- 2004
Results for articulated objects, which show that the proposed method can categorize and segment unfamiliar objects in differ- ent articulations and with widely varying texture patterns, even under significant partial occlusion.
The Visual Object Tracking VOT2015 Challenge Results
- M. Kristan, Jiri Matas, Zhe Chen
- Computer ScienceIEEE International Conference on Computer Vision…
- 7 December 2015
The Visual Object Tracking challenge 2015, VOT2015, aims at comparing short-term single-object visual trackers that do not apply pre-learned models of object appearance and presents a new VOT 2015 dataset twice as large as in VOT2014 with full annotation of targets by rotated bounding boxes and per-frame attribute.
The Visual Object Tracking VOT2017 Challenge Results
- M. Kristan, A. Leonardis, Zhiqun He
- Computer ScienceIEEE International Conference on Computer Vision…
- 1 October 2017
The Visual Object Tracking challenge VOT2017 is the fifth annual tracker benchmarking activity organized by the VOT initiative. Results of 51 trackers are presented; many are state-of-the-art…
A Novel Performance Evaluation Methodology for Single-Target Trackers
- M. Kristan, Jiri Matas, Luka Cehovin
- Computer ScienceIEEE Transactions on Pattern Analysis and Machine…
- 4 March 2015
The requirements are the basis of a new evaluation methodology that aims at a simple and easily interpretable tracker comparison and a fully-annotated dataset with per-frame annotations with several visual attributes, which is the largest benchmark to date.
The Seventh Visual Object Tracking VOT2019 Challenge Results
- M. Kristan, Jiri Matas, Zihan Ni
- Computer ScienceIEEE/CVF International Conference on Computer…
- 1 October 2019
The Visual Object Tracking challenge VOT2019 is the seventh annual tracker benchmarking activity organized by the VOT initiative. Results of 81 trackers are presented; many are state-of-the-art…
The Visual Object Tracking VOT2015 Challenge Results
- M. Kristan, Jiri Matas, Zhibin Hong
- Computer Science
- 2018
The Visual Object Tracking challenge 2015, VOT 2015, aims at comparing short-term single-object visual trackers that do not apply pre-learned models of object appearance and presents a new VOT2015 dataset twice as large as in VOT2014 with full annotation of targets by rotated bounding boxes and per-frame attribute.
A Continual Learning Survey: Defying Forgetting in Classification Tasks
- Matthias De Lange, Rahaf Aljundi, T. Tuytelaars
- Computer ScienceIEEE Transactions on Pattern Analysis and Machine…
- 18 September 2019
This work focuses on task incremental classification, where tasks arrive sequentially and are delineated by clear boundaries, and develops a novel framework to continually determine the stability-plasticity trade-off of the continual learner.
...
...